Photoplethysmography-based pulse wave analysis using a wearable device

ABSTRACT

Disclosed are devices and methods for non-invasively measuring arterial stiffness using pulse wave analysis of photoplethysmogram data. In some implementations, wearable biometric monitoring devices provided herein for measuring arterial stiffness have the ability to automatically and intelligently obtain PPG data under suitable conditions while the user is engaged in activities or exercises. In some implementations, wearable biometric monitoring devices are provided herein with the ability to remove PPG data variance caused by factors unrelated to arterial stiffness. In some implementations, wearable biometric monitoring devices have the ability to perform PWA while accounting for the user&#39;s activities, conditions, or status.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefits under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/286,224, entitled:PHOTOPLETHYSMOGRAPHY-BASED PULSE WAVE ANALYSIS USING A WEARABLE DEVICE,filed Jan. 22, 2016, which is herein incorporated by reference in itsentirety for all purposes.

BACKGROUND

This disclosure provides devices and methods for estimating arterialstiffness using pulse wave analysis, particularly withphotoplethysmograhy.

Arteries harden as a result of vascular ageing and other physiologicalfactors such as pathologies and diets. Arterial stiffness indicatescardiovascular dysfunction and is an established independent predictorof cardiovascular risk.

The contraction of the left ventricle pumps blood into the aorticartery, causing blood volume and blood pressure to change in thearteries, forming an arterial pressure wave or pulse wave in theperipheral arteries. Arterial stiffness can be non-invasively estimatedby pulse wave analysis (PWA) of blood pressure data obtained by atonometer, or blood volume data of a photoplethysmogram (PPG) sensor.PWA involves obtaining pulse waveforms from pulse wave data, andextracting and analyzing morphological features of the pulse waveforms.Morphological features of the pulse waveforms, such as augmentationindex, reflection index, pulse transit time, are shown to correlate witharterial stiffness.

PWA is conventionally performed at a doctor's office by a medicalprofessional (e.g., tonometer-based PWA), or through a take-home deviceadministered to user for a few days. The administration of the analysismay require stringent procedures or measuring conditions. Conventionalhardware such as a tonometer or a finger-based PPG for performing PWAcan be cumbersome to use. As a result, PWA data collection and resultsmay be compromised by various extraneous factors in a nonclinicalsetting. It may be impractical or inconvenient to obtain repeatedmeasurements through a sufficiently long period of time. Therefore,there are needs for devices and methods that allow convenient,noninvasive, and accurate measuring of arterial stiffness.

SUMMARY

Devices and methods are provided for non-invasively measuring arterialstiffness using PWA of PPG data. In some implementations, wearablebiometric monitoring devices for measuring arterial stiffness have theability to automatically and intelligently obtain PPG data undersuitable conditions while the user is engaged in activities orexercises, providing good-quality PPG data for PWA while conservingpower of the wearable biometric monitoring devices. In someimplementations, wearable biometric monitoring devices can remove PPGdata variance caused by factors unrelated to arterial stiffness. In someimplementations, wearable biometric monitoring devices have the abilityto perform PWA while accounting for the user's activities, conditions,or status, thereby providing robust and/or customized measurements ofarterial stiffness.

One aspect of the disclosure provides a biometric monitoring device formeasuring arterial stiffness. The device include: (a) a wearable fixingstructure configured to attach to a user and/or a user's apparel in amanner allowing the user to wear the biometric monitoring device whileperforming activities; (b) an inertial sensor configured to generateinertial data measuring movement experienced by the biometric monitoringdevice; (c) a photoplethysmogram (“PPG”) sensor to generate PPG sensordata; and (d) one or more processors. The processors are configured to:(i) obtain the inertial data from the inertial sensor, (ii) obtain thePPG sensor data from the PPG sensor, (iii) filter the PPG sensor datausing information obtained from the inertial data, and (iv) determineone or more morphological features of a pulse waveform derived from thefiltered PPG sensor data, and/or transmit the filtered PPG sensor datato a device configured to determine the one or more morphologicalfeatures of the pulse waveform from the filtered PPG sensor data,wherein the one or more morphological features are related to arterialstiffness.

In some implementations, the one or more processors are furtherconfigured to, before (ii): determine that one or more conditions forcollecting pulse waveform data from the user are satisfied; and trigger,based on the determination that the one or more conditions aresatisfied, the PPG sensor to obtain the PPG sensor data.

In some implementations, the one or more conditions include the user'sactivity or location. In some implementations, the one or moreprocessors are configured to determine that the inertial data matches anactivity profile. In some implementations, the activity profile is foran activity such as resistance training, aerobic exercising, endurancetraining, sitting, working, and sleeping. In some implementations, theone or more conditions include the inertial data being indicative of thebiometric monitoring device being still for a period of time. In someimplementations, triggering the PPG sensor to obtain the PPG sensor dataincludes increasing a sampling rate in which the PPG sensor operates.

In some implementations, the inertial sensor includes an accelerometer.

In some implementations, the one or more processors are furtherconfigured to determine that the inertial data matches an orientationprofile.

In some implementations, (ii) is performed based on the one or moreprocessor determining, from the inertial data, that the biometricmonitoring device has experienced movement below a movement thresholdfor a period of time and/or the inertial data matches an orientationprofile.

In some implementations, the wearable fixing structure includes a strapfor attaching to the user's limb. In some implementations, the wearablefixing structure includes a strap for attaching to the user's wrist. Insome implementations, the strap and/or the one or more sensors areconfigured to permit the PPG sensor to obtain the PPG sensor data fromthe user's ulnar and/or radial artery.

In some implementations, the biometric monitoring device furtherincludes one or more sensors selected from the group consisting of atemperature sensor, a strain sensor, and a pressure sensor. In someimplementations, the one or more processors are further configured toperform wave normalization on the pulse waveform using temperature datafrom the temperature sensor. In some implementations, the one or moreprocessors are further configured to perform wave normalization on thearterial waveform using pressure data from the strain sensor or thepressure sensor.

In some implementations, the one or more processors are furtherconfigured to use the inertial data to reject motion artifact.

In some implementations, the biometric monitoring device furtherincludes an enclosure enclosing the inertial sensor, the PPG sensor, andthe one or more processors.

In some implementations, the one or more processors are furtherconfigured to trigger repeatedly obtaining the PPG sensor data fordetermining pulse waveforms, while the user wears the biometricmonitoring device.

In some implementations, the one or more processors are configured totrigger obtaining the PPG sensor data for at least X pulse waveformsover a period of at most about Y seconds. In some implementations, theone or more processors are configured to trigger obtaining the PPGsensor data for at least X pulse waveforms every day over a period of atleast Y days.

In some implementations, the one or more processors are furtherconfigured to determine the user's heart rate using the PPG sensor dataand trigger obtaining the PPG sensor data for a number of pulsewaveforms, wherein the number of pulse waveforms depends at least partlyon the user's heart rate. In some implementations, the number of pulsewaveforms increases as the user's heart rate increases.

In some implementations, the one or more processors are furtherconfigured to determine the user's respiration rate and triggerobtaining the PPG sensor data for a number of pulse waveforms, whereinthe number of pulse waveforms depends at least partly on the user'srespiration rate. In some implementations, the number of pulse waveformsincreases as the user's respiration rate increases.

In some implementations, the one or more processors are configured totrigger obtaining the PPG sensor data at a schedule based on activitytypes or locations of the user.

In some implementations, the one or more processors are furtherconfigured to analyze the pulse waveform to obtain an estimate of theuser's arterial stiffness.

Another aspect of the disclosure provides a method for measuringarterial stiffness of a user. The method employs a biometric monitoringdevice including one or more processors, a photoplethysmogram (PPG)sensor, and one or more additional biometric sensors. The methodinvolves: (a) determining that one or more conditions for collectingpulse waveform data from the user are satisfied; (b) triggering, basedon the determination of (a), the PPG sensor to collect pulse waveformdata from the user; and (c) obtaining one or more measurementsindicating arterial stiffness from the collected pulse waveform data ofthe user.

In some implementations, determining that the one or more conditions forcollecting pulse waveform data from the user are satisfied includes:obtaining biometric data regarding the user from the one or moreadditional biometric sensors; and analyzing the biometric data todetermine that the one or more conditions for collecting the pulsewaveform data from the user are satisfied. In some implementations, theone or more conditions include one or more of the following: a motionlevel of the user being below a motion threshold, an activity of theuser being a specific activity type, a body temperature of the usermeeting a criterion, noise in previously obtained pulse waveform databeing above a noise threshold, a force indicative of a tightness betweenthe biometric monitoring device and the user meeting a criterion,historical activity data meeting a past activity criterion, a placementof the PPG sensor is determined to be appropriate, an orientation of thedevice meeting a criterion, and a posture of the user meeting acriterion. In some implementations, the placement of the sensor isdetermined to be appropriate when the biometric data indicates that thePPG sensor is placed on the user's body near an artery.

In some implementations, the method further includes recordinghistorical activity data of the user. In some implementations, thehistorical activity data include the amount, time and type of the lastactivity and the time between the last activity and the time when thepulse waveform data is collected.

In some implementations, the one or more measurements indicatingarterial stiffness are selected from the group consisting of:augmentation index, reflection index, stiffness index, accelerationplethysmogram features, and any combinations thereof.

In some implementations, the one or more measurements indicatingarterial stiffness are obtained from cross-correlation of pulsewaveforms with preexisting templates or decomposition of the pulsewaveforms using one or more approximation methods. In someimplementations, the one or more approximation methods include curvefitting.

In some implementations, triggering the PPG sensor includes operatingthe PPG sensor at a sampling rate equal to or higher than about 150 Hz.In some implementations, triggering the PPG sensor includes increasing asampling rate of the PPG sensor.

In some implementations, the one or more conditions for collecting pulsewaveform data includes a motion level of the user being below a motionthreshold.

In some implementations, triggering the PPG sensor includes increasingan intensity of light emitted by an LED of the PPG sensor.

In some implementations, triggering the PPG sensor includes samplingfrom LEDs that are spaced further apart than LEDs used for heart rate orpulse oxygenation.

In some implementations, triggering the PPG sensor includes operatingthe PPG sensor at a sampling rate equal to or higher than about 150 Hzin two or more intervals distributed in two or more different hours in aperiod of time.

In some implementations, the biometric monitoring device is configuredas a wrist-worn biometric monitoring device. In some implementations,the biometric monitoring device includes one or more PPG sensorsdisposed on a band configured to be worn around a wrist. In someimplementations, the biometric monitoring device includes a plurality ofPPG sensors disposed on a band configured to be worn around a wrist. Insome implementations, the biometric monitoring device includes a displayand one or more PPG sensors, wherein the display and at least one of thePPG sensors are configured to be positioned on substantially oppositesides of a wrist when worn on the wrist.

In some implementations, the one or more additional biometric sensorsare selected from the group consisting of: an accelerometer, agyroscope, an altimeter, a temperature sensor, a force sensor, apressure sensor, a galvanic skin response sensor, a magnetometer, a GPSsensor, an ambient light sensor, and any combinations thereof.

An additional aspect of the disclosure relates to a method implementedat a biometric monitoring device including one or more processors, aphotoplethysmogram (PPG) sensor, and one or more additional biometricsensors, for measuring arterial stiffness of a user. The methodincludes: operating the PPG sensor to obtain pulse waveform data fromthe user; operating the one or more additional biometric sensors toobtain biometric data other than PPG data; extracting a plurality ofpulse waveforms from the pulse waveform data; normalizing the pluralityof pulse waveforms; aggregating the plurality of pulse waveforms; andobtaining one or more measurements indicating arterial stiffness fromthe aggregate pulse waveform data.

In some implementations, normalizing the pulse waveform data includesscaling the plurality of pulse waveforms to a duration in time or apulse waveform cycle and an amplitude.

In some implementations, normalizing the pulse waveform data includesadjusting the pulse waveform data for one or more factors determinedfrom the other biometric data. In some implementations, normalizing thepulse waveform data includes: modeling a relationship between (a) one ofthe one or more factors, and (b) a variable related to the amplitude ofthe pulse waveform; applying the one factor of the user to the modeledrelationship to obtain a normal pulse waveform or features thereof; andscaling the plurality of pulse waveforms or features thereof based onthe normal pulse waveform or features thereof. In some implementations,the variable related to the amplitude of the pulse waveform is selectedfrom the group consisting of: blood volume, arterial compliance, bloodflow, or blood perfusion.

In some implementations, obtaining one or more measurements indicatingarterial stiffness includes applying the one or more factors, as well asthe aggregate pulse waveform data, to a model to obtain the one or moremeasurements.

In some implementations, obtaining one or more measurements indicatingarterial stiffness including: (a) selecting a model based on the one ormore factors, and (b) applying the aggregate pulse waveform data to theselected model to obtain the one or more measurements indicatingarterial stiffness.

In some implementations, the one or more factors are selected from thegroup consisting of user activity, user posture, device orientation,user body temperature, and sensor pressure.

In some implementations, the method further includes filtering out amotion signal component from the pulse waveform data.

In some implementations, the method further includes removing data of atleast one outlier pulse waveform from the normalized pulse waveform databefore aggregating the plurality of pulse waveforms.

In some implementations, removing data of at least one outlier pulsewaveform includes: obtaining one or more pulse waveform templates, eachtemplate being derived from one or more pulse waveforms obtained fromthe user; identifying the at least one outlier pulse waveform bycomparing the user's pulse waveforms to the one or more pulse waveformtemplates; and removing data of the at least one outlier pulse wave.

In some implementations, a pulse waveform template is obtained from theuser when the user is in a condition selected from the group consistingof: sleeping, orienting a wrist wearing the biometric monitoring devicein a defined direction, performing an exercise, having recentlyperformed an exercise, having no recent exercises or steps, having noerratic motions, physiological stress, elevated heart rate or low heartrate variability, having not consumed food or drugs recently and anycombinations thereof.

In some implementations, removing data of at least one outlier pulsewaveform includes determining cross-correlation scores among theplurality of pulse waveforms and removing data of at least one pulsewaveform having cross-correlation scores lower than a criterion value.In some implementations, the criterion value is a score at a relativethreshold generated from the cross-correlation scores.

In some implementations, the method further includes, prior to obtainingthe one or more measurements indicating arterial stiffness, averagingdata across multiple pulse waveforms.

In some implementations, the method further includes presenting the oneor more measurements indicating arterial stiffness on the biometricmonitoring device.

A further aspect of the disclosure relates to another method implementedat a biometric monitoring device including one or more processors, aphotoplethysmogram (PPG) sensor, and one or more additional biometricsensors, for evaluating arterial stiffness of a user. The methodincludes: operating the PPG sensor to obtain pulse waveform data fromthe user; operating the one or more additional biometric sensors toobtain other biometric data; obtaining one or more measurements of oneor more pulse waveform morphological features using the pulse waveformdata; obtaining one or more measurements of one or more additionalvariables derived from the other biometric data; and estimating thearterial stiffness of the user using the one or more measurements of thepulse waveform morphological features and the one or more measurementsof the additional variables.

In some implementations, the additional variables are selected from thegroup consisting of: weight measurement, temperature, respirationmeasurements, blood pressure measurements, sleep stage, user height,user posture, orientation of the biometric monitoring device, anactivity pattern recently detected, and variables derived therefrom.

In some implementations, estimating the arterial stiffness of the userincludes applying the one or more measurements of the pulse waveformmorphological features to a model, wherein the model relates the pulsewaveform morphological features to an arterial stiffness measurement.

In some implementations, the method further includes presentinginformation indicating the estimated arterial stiffness on the biometricmonitoring device.

An additional aspect of the disclosure relates to an additional methodimplemented at a biometric monitoring device including one or moreprocessors and a photoplethysmogram (PPG) sensor. The method includes:operating the PPG sensor to obtain pulse waveform data from the user;obtaining one or more measurements of pulse waveform morphologicalfeatures of the user using the pulse waveform data; providing a templateof the pulse waveform morphological features; comparing the one or moremeasurements of pulse waveform morphological features of the user andthe pulse waveform morphological features of the template; anddetermining an arterial stiffness measurement of the user based on oneor more comparison results from comparing the pulse waveformmorphological features of the user and the template.

In some implementations, the template includes statistics that quantifythe pulse waveform morphological features of a plurality of users. Insome implementations, the plurality of users and the user belong to thesame group determined by clustering. In some implementations, theclustering is based on a weight measurement and/or age of the pluralityof users.

In some implementations, determining an arterial stiffness measurementof the user includes applying the comparison results to a model toobtain the arterial stiffness measurement of the user, wherein the modeltakes the comparison results as inputs and provides a value of anarterial stiffness measurement as an output. In some implementations,the model includes a general linear model, a non-linear model, aregression tree model, or a neural network model.

In some implementations, the template includes pulse waveformmorphological features of a plurality of pulse waveforms collected fromthe user.

In some implementations, providing the template includes building thetemplate using features of a plurality of pulse waveforms obtained fromthe user.

In some implementations, the method further includes displaying thearterial stiffness measurement of the user on the on the biometricmonitoring device.

These and other objects and features of the present disclosure willbecome more fully apparent from the following description, withreference to the associated drawings and appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a representation of a pulse waveform that can be obtainedfrom PPG data and the first and second derivatives of the pulsewaveform.

FIG. 2 shows a flow chart illustrating a process 200 for determiningarterial stiffness implemented by a wearable biometric monitoring devicein some implementations.

FIG. 3 shows a process flow chart for preprocessing pulse waveform datathat can be performed by biometric monitoring device according to someimplementations.

FIG. 4 shows example of multiple y and/or x normalized pulse waveforms(gray) being combined into a more reliable pulse waveform (black).

FIG. 5 illustrates a process for performing PWA to determine arterialstiffness.

FIG. 6 shows four pulse waveform templates for the four clusters withdifferent morphological features.

FIG. 7 illustrates an example portable monitoring device which enablesuser interaction via a user interface.

FIG. 8 is a block diagram showing components of a biometric monitoringdevice in such an implementation.

FIG. 9A illustrates an example portable monitoring device which may besecured to the user through the use of a band.

FIG. 9B provides a view of the example portable monitoring device ofFIG. 9A which shows the skin-facing portion of the device.

FIG. 9C provides a cross-sectional view of the portable monitoringdevice of FIG. 9A.

FIG. 10A provides a cross sectional view of a sensor protrusion of anexample portable monitoring device.

FIG. 10B depicts a cross sectional view of a sensor protrusion of anexample portable monitoring device; this protrusion is similar to thatpresented in FIG. 10A with the exception that the light sources andphotodetector are placed on a flat and/or rigid PCB.

FIG. 10C provides another cross-sectional view of an example PPG sensorimplementation.

FIG. 11A illustrates an example of one potential PPG light source andphotodetector geometry.

FIGS. 11B and 11C illustrate examples of a PPG sensor having aphotodetector and two LED light sources.

FIG. 12 Illustrates an example of an optimized PPG detector that has aprotrusion with curved sides so as not to discomfort the user.

FIG. 13A illustrates an example of a portable monitoring device having aband; optical sensors and light emitters may be placed on the band.

FIG. 13B illustrates an example of a portable biometric monitoringdevice having a display and wristband. Additionally, optical PPG (e.g.,heart rate) detection sensors and/or emitters may be located on the sideof the biometric monitoring device. In one embodiment, these may belocated in side-mounted buttons.

FIG. 14 depicts a user pressing the side of a portable biometricmonitoring device to take a heart rate measurement from a side-mountedoptical heart rate detection sensor. The display of the biometricmonitoring device may show whether or not the heart rate has beendetected and/or display the user's heart rate.

FIG. 15 illustrates functionality of an example biometric monitoringdevice smart alarm feature.

FIG. 16 illustrates an example of a portable biometric monitoring devicethat changes how it detects a user's heart rate based on how muchmovement the biometric monitoring device is experiencing.

FIG. 17 illustrates an example of a portable biometric monitoring devicethat has a bicycle application on it that may display bicycle speedand/or pedaling cadence, among other metrics.

FIG. 18A illustrates an example block diagram of a PPG sensor which hasa light source, light detector, ADC, processor, DAC/GPIOs, and lightsource intensity and on/off control.

FIG. 18B illustrates an example block diagram of a PPG sensor that issimilar to that of FIG. 18A which additionally uses a sample-and-holdcircuit as well as analog signal conditioning.

FIG. 18C illustrates an example block diagram of a PPG sensor that issimilar to that of FIG. 18A which additionally uses a sample-and-holdcircuit.

FIG. 18D illustrates an example block diagram of a a PPG sensor havingmultiple switchable light sources and detectors, light sourceintensity/on and off control, and signal conditioning circuitry.

FIG. 18E illustrates an example block diagram of a PPG sensor which usessynchronous detection. To perform this type of PPG detection, it has ademodulator.

FIG. 18F illustrates an example block diagram of a PPG sensor which, inaddition to the features of the sensor illustrated in FIG. 18A, has adifferential amplifier.

FIG. 18G illustrates an example block diagram of a PPG sensor which hasthe features of the PPG sensors shown in FIGS. 18A-18F.

FIG. 19A is a schematic diagram of an example of a portable biometricmonitoring device having a heart rate or PPG sensor, motion sensor,display, vibromotor, and communication circuitry which is connected to aprocessor.

FIG. 19B is a schematic diagram of an example of a portable biometricmonitoring device having a heart rate or PPG sensor, motion sensor,display, vibromotor, location sensor, altitude sensor, skinconductance/wet sensor and communication circuitry which is connected toa processor.

FIG. 19C is a schematic diagram of an example of a portable biometricmonitoring device having physiological sensors, environmental sensors,and location sensors connected to a processor.

DETAILED DESCRIPTION

Various morphological features of the pulse waveforms are correlatedwith arterial structures and functions including arterial stiffness.Morphological features of a pulse waveform as used herein includevarious aspects of the shape of the waveform and information derivedtherefrom including the slopes of various portions of the waveform, timeseparation between such portions, amplitudes of such portions, and thelike. Morphological features of the pulse waveform also include variousaspects of the shape of the derivatives of the waveform including afirst derivative, a second derivative, and higher order derivatives.Unless otherwise qualified, any one or more of these features may beused in the analyses described herein.

In some context not specific to the disclosure herein, the term pulsewaveform can be interpreted as closely related to the shape of a PPGgraph reflecting blood volume change caused by pulsed blood flow, whilethe term pulse wave may be interpreted as more closely related to theblood volume change caused by pulsed blood flow. However, the two termsare used interchangeably herein unless specified as different. Forinstance, pulse waveform analysis and pulse wave analysis are usedinterchangeably herein.

Introduction

PWA involves obtaining and analyzing morphological features of pulsewaveforms, which may be obtained from, e.g., PPG data. Morphologicalfeatures of pulse waveform (or arterial pressure waveform) can provideuseful information relating to arterial structure and function,including arterial stiffness. Arterial stiffness is an importantindicator of cardiovascular diseases and a useful predictor ofcardiovascular risks. Various implementations of this disclosure provideone or more improvements below that are lacking in existing technologyfor measuring and monitoring arterial stiffness.

Various morphological features of the pulse waveforms are correlatedwith arterial structures and functions including arterial stiffness.Morphological features of a pulse waveform as used herein includevarious features of the shape of the waveform and information derivedtherefrom. Morphological features of the pulse waveform also includevarious features of the shape of the derivatives of the waveform.

PWA can be performed at a doctor's office by a professional or at homeusing a take-home device administered to user for a few days when theuser is preferably disengaged from other activities. However, performingPWA at a doctor's office presents a number of difficulties andconstraints, such as restrictions on when, where, and how often PWA canbe performed. In comparison, some embodiments discussed herein providedevices and methods for estimating arterial stiffness using PPG-basedPWA that are non-invasive, easy to operate, and suitable for takingmeasurements over a number of contexts, such as when a user is engagedin activities in nonclinical settings, e.g., exercising or sleeping.

Further, some embodiments can provide wearable biometric monitoringdevices that are small, light, portable, and/or non-intrusive, and canbe worn during daily activities and exercise. In some embodiments, thewearable biometric monitoring device can be configured as a wrist-worndevice, and equipped with a rechargeable or non-rechargable batterycapable of powering the device to operate for hours, days, weeks, ormonths per charge. Such configuration and function of the biometricmonitoring device allows repeated PPG data collection and PWA over along time and during many different activities and conditions. Examplesof such biometric monitoring devices are presented in FIGS. 7, 8 9, and13. In some applications, long-term trending and personalized models canbe created to provide features related to a user's physiology.

In some implementations, the wearable biometric monitoring deviceintegrates multiple biometric sensors including a PPG sensor, whichsensors are enclosed in a housing of a wearable device. Using biometricdata obtained from one or more biometric sensors other than the PPGsensor, the device can provide robust arterial stiffness measurementsusing PPG-based pulse waveform data under different conditions bycanceling noise and controlling factors irrelevant to arterial stiffnessmeasurements. The robustness of the arterial stiffness measurements ofthe device allows the device to be used in nonclinical settings when theuser is engaged in daily activities or exercises, without requiring theadministration by a professional.

The robustness and the convenience of the wearable biometric monitoringdevices are conducive to measuring arterial stiffness by different typesof users (e.g., a user type may be determined by age and/or weightmeasurement such as weight, body-mass-index (BMI), body fat percentage,and the like) or under different conditions (e.g., skin temperature ofthe user, pressure between the PPG sensor and the user's skin contactarea, currently engaged activity, and recently completed activity). Toimprove the usefulness of the arterial stiffness information to thedifferent types of users under different conditions, someimplementations of the biometric monitoring device can perform PWA thatare normalized or tailored for a specific user type or condition. Insome implementations, different user types have different values orranges of values of PWA measurements associated with a condition ofinterest. For instance, a 5% increase of the augmentation index of apulse waveform for a first type of user may have a more severe impactthan for a second type of user. In some implementations, the biometricmonitoring device will alarm the first type of user for such anincrease, but will not alarm the second type of user. In anotherexample, if a user belongs to a first group, the user's pulse waveformcan be normalized relative to the first group. Then the morphologicalfeatures of the normalized pulse waveforms are compared to a template ofthe first group. Group-specific measurements of arterial stiffness canbe obtained from the comparison. In another example, algorithms fordetermining arterial stiffness may differ depending on the group towhich the user belongs. For example, the transfer function mapping pulsemorphological features to arterial stiffness may be different for agroup consisting of users above 60 years old as compared to the group ofusers younger than 60 years old.

Some embodiments of the biometric monitoring device discussed herein canperform PWA that are normalized or customized for a specific usercondition. For instance, in some implementations, the wearable biometricmonitoring device includes one or more of accelerometers or inertialsensors, skin temperature sensors, and force sensors. Using theaccelerometer signal or inertial data, frequency components or periodiccomponents in PPG signal corresponding to those in accelerometer signalor inertial data can be filtered out from the PPG signal beforemeasuring arterial stiffness. Moreover, skin temperature can affectperfusion of peripheral arteries and measurement of skin temperatureallows for appropriate normalization of pulse waveform data beforedetermining morphological features of pulse waveforms or arterialstiffness measurements. Furthermore, a force sensor can give anindication of band tightness which allows for non-linearly normalizingfeatures based on the changing compliance of the arteries due to thevariable sensor contact force.

Such flexible capabilities allow the biometric monitoring device toprovide individualized and customized measurements of arterial stiffnessthat are indicative of an individual user's health when the user isunder a specific condition or of a specific user type.

In a wearable biometric monitoring device with a form factor that iseasy and comfortable to use, the battery powering the biometricmonitoring device has limited bulk (mass and volume) and limitedcapacity. In some implementations, the wearable biometric monitoringdevice can determine one or more conditions when the PPG sensor shouldbe triggered to collect PPG data and perform pulse waveform analysis.For example, in some implementations, the wearable biometric monitoringdevice includes an inertial sensor and it triggers its PPG sensor toobtain data for pulse waveform analysis if it detects that the user isrelatively still or engaged in a specific activity (e.g., sleeping orsitting). The device can determine the user's activity using theinertial data. For instance, it detects or infers that the user isrelatively still when inertial data is indicative of the biometricmonitoring device have been still for a period of time. When the one ormore conditions are not met, in some implementations, the devicediscontinues the PPG sensor's collection of data for pulse waveformanalysis to conserve energy. In some implementations, the inertialsensor includes one or more accelerometers.

For higher fidelity of the pulse waveform, PPG signal can be sampled ata high rate (>25 Hz e.g. 100 Hz) and/or oversampled at an even higherrate and then averaged to reduce the noise of a waveform at a givensampling rate (e.g. 100 Hz). This can provide better representation ofthe wave's morphological details by reducing noise and increasing timeresolution. The improved representation enables the detection of subtlechanges to a user's pulse morphology and the detection of subtlemorphological features, such as the dicrotic notch in a pulse waveform.In some implementations, when the device determines a condition forcollecting PPG data is not met, the sampling rate or light intensity ofa light source of the PPG sensor can be reduced to perform otherfunctions such as heart rate measurement or skin proximity detection. Byoperating the PPG sensor in different modes under different conditions,the wearable biometric monitoring device can achieve utilities whilepreserving battery power.

Pulse Wave Analysis and Arterial Stiffness Measurements

Various implementations of the disclosure provide devices and methodsfor performing pulse wave analysis (PWA) on PPG data to obtainmorphological features of pulse waveforms and obtain measurements andestimates of arterial stiffness using the obtained morphologicalfeatures.

FIG. 1 shows, in the top panel, a representation of a pulse waveformthat can be obtained from PPG data. The horizontal axis represents time,and the vertical axis represents normalized pulse pressure or signalrelated to pulse pressure. For instance, voltage of a sensor may bemapped to blood volume, which can then be mapped to blood pressure.Various morphological features of the pulse waveforms are correlatedwith arterial structures and functions including arterial stiffness.Note that the waveform shown in FIG. 1 is a normalized waveform in the Yaxis. All of the techniques described herein may also be used on a nonY-normalized pulse waveform. All of the techniques described here mayalso be used on an X-normalized (or time-normalized) pulse waveformwhere the length of each pulse is scaled to 1 or some normal value.There are various ways to normalize signal. Any single normalizationtechnique or any combination of normalization techniques may be employedto derive features for an arterial stiffness measurement. Some of themorphological features of the pulse waveform that may be obtained invarious implementations of the disclosure are described next.

The pulse waveform 101 shown in the top panel, as an example, has twodistinct peaks 102 and 102, with P1 (102) on the left being higher thanP2 (104) on the right. P1 (102) is caused by the ejection of blood fromthe left ventricle, corresponding to early systolic pressure. P2 (104)is caused by a reflection wave, corresponding to late systolic pressure.It has been shown that ageing may cause arterial walls to harden, whichcan give rise to a more pronounced reflection wave.

Pulse waveforms observed at the peripheral arteries have differentshapes and morphological features than those observed at or near theaortic artery. For instance, in some samples under some conditions, awaveform observed at a peripheral artery is more likely to have twodistinct peaks, where a waveform observed near the aortic artery aremore likely to have two peaks merged into a left shoulder and a rightshoulder, with the right shoulder sometimes being higher than the leftshoulder in some cases. The example shown here is more representative ofa pulse waveform observed at a peripheral artery, such as at the wrist.

The base of the waveform near zero corresponds to diastolic pressure.The height of the early systolic pressure (P1) relative to the diastolicpressure is labeled as “a” in the figure, which is also known as thepulse pressure. The height of the late systolic pressure (P2) relativeto the diastolic pressure is labeled as “b” in the figure, which is alsoknown as the augmentation pressure. The ratio b/a is known as theaugmentation index, or peripheral augmentation index for a peripheralpulse wave. Augmentation index correlates positively with arterialstiffness. Augmentation index is a sensitive marker of arterial statusand a predictor of adverse cardiovascular events in a variety of patientpopulations. It has been shown that higher augmentation index isassociated with target organ damage. As shown in some studies,augmentation index can distinguish between the effects of differentvasoactive medications while upper arm blood pressure and pulse wavevelocity do not.

The time difference between P1 and P2, labeled as “d,” indicates thetime difference between the peak of the ejection wave and the peak ofthe reflection wave. It is known as reflection transit time, which cancorrespond to pulse transit time in some measurements. Pulse transittime and pulse wave velocity have been shown to correlate with bloodpressure and arterial stiffness.

An inflection point at the upstroke of the second peak provides amorphological feature with a pressure of “c”, which may be used toobtain a reflection index in some cases of PWA. As with augmentationindex, reflection index has a positive correlation with arterialstiffness. Similarly to the time measurement from P1 (102) to P2 (104)being equal to “d”, a time between P1 and inflection point 106 or P2 andinflection point 106 can be used in an algorithm to determine arterialstiffness.

The middle panel of FIG. 1 shows the first derivative of the pulsewaveform 103 in the top panel. The bottom panel of FIG. 1 shows thesecond derivative 105. It is illustrated here that morphologicalfeatures of the derivatives of a pulse waveform may be used to identifyrelevant morphological features in the pulse waveform. For instance, thepositive-to-negative zero crossing points 108 and 110 of the firstderivative corresponds to the local maxima P1 and P2 (102 and 104) inthe pulse waveform. The second positive-to-negative zero crossing point114 of the second derivative corresponds to the local maxima of thefirst derivative 112 and the inflection point of the pulse waveform 106.

The above mentioned morphological features are relevant for arterialstiffness measurements. Examples of other useful morphological featuresinclude but are not limited to: ejection duration, heart rate, pressureat first shoulder, pressure at second shoulder, pressure at end systole,augmented pressure, mean diastolic pressure, mean arterial pressure,mean systolic pressure, tension time index, diastolic time index,subendocardial variability ratio, maximum rate of rise, reference age,acceleration features (e.g., those determined from the derivatives), andstiffness index. It is to be appreciated that some implementations maymeasure one or more of the pressure measurements just listed via voltageor volume signals generated by a PPG sensor (or PPG sensors), instead ofpressure signals obtained from a pressure sensor. The voltage or volumesignals obtained via a PPG sensor may be normalized. To illustrate,voltage values generated by a PPG sensor may be mapped to volume values,which, in turn, may be non-linearly mapped to pressure values. As such,the morphological features of pressure described above may be indicatedby voltage or volume signals generated by a PPG sensor.

One aspect of the disclosure provides a wearable biometric monitoringdevice that is capable of intelligently determining one or moreconditions and obtaining PPG data suitable for PWA if the one or moreconditions are satisfied. The obtained PPG data may be preprocessed toobtain pulse waveforms in, e.g., a standard or normalized format, whichare then used to perform PWA to determine arterial stiffness. FIG. 2shows a flow chart illustrating a process 200 for determining arterialstiffness implemented by a wearable biometric monitoring device in someimplementations. The process 200 starts by determining whether thecurrent time is in a defined pulse waveform capture time period. Forinstance, the defined data capturing may be a specific time of day, aspecific day of the week, a specific time from a defined time, or aspecific time since the last data capture session, etc. This allows thebiometric monitoring device to obtain PPG data based on a schedule ortime considerations. The schedule may be based on the user's history(e.g., typical times of day or the week when the user is not engaged instrenuous activity), a processor's available bandwidth for performingPWA, and the like. If the current time is not within a defined capturetime period, the device does not trigger PPG data collection and waitfor the next time interval to examine the condition again. See decisionblock 202.

However, if it is determined that the current time is in a defined timeperiod for obtaining PPG data for PWA, the process further determinewhether one or more user conditions suitable for collecting PPG data toobtain pulse waveforms are satisfied. See decision block 204. If the oneor more conditions are not satisfied, the process loops back to checkingwhether the next instance of time is in the defined pulse waveformcapturing time period. If the one or more conditions are satisfied, theprocess proceeds to trigger the PPG sensor to obtain PPG data in amanner that is suitable for generating pulse waveform data for PWA. Seeblock 206.

Although decision block 202 and decision block 204 are shownsequentially, they may be processed in reversed order, in alternative,or in conjunction. Indeed, unless specified otherwise, the sequence ofmany blocks in the flow charts herein is generally illustrative and canbe reordered.

In some implementations, triggering the PPG sensor to collect pulsewaveform data requires activating a light source of the PPG sensor toemit light pulses in a particular frequency and intensity. Because PWArequires extracting morphological features from pulse waveforms, it isdesirable to have high sampling rate and sufficient signal strength. Invarious implementations, the sampling rate is at least about 25 Hz, 50Hz, 100 Hz, 150 Hz, 200 Hz, or 400 Hz. In some implementations, the PPGsensor operates at a lower sampling frequency (e.g., less than about 25Hz) before entering pulse waveform data collection, and increasessampling rate when triggered to collect pulse waveform data. In someimplementations, triggering the PPG sensor includes sampling from one ormore LEDs that are spaced further apart than the LEDs which are used forother purposes (e.g. heart rate measurement). Using LEDs that are spacedfurther apart may improve the PPG signal for pulse waveform analysisand/or cause the PPG sensor to sample a volume of tissue that is deeperthan closely spaced LEDs. Additionally, in some implementations,triggering the PPG sensor includes using an LED with differentwavelength, e.g. red or infrared, compared to the LED used to acquireheart rate data.

In some implementations, the one or more conditions for triggering thePPG sensor include motion of the user. If the user's motion level isbelow a criterion value (e.g., a motion threshold), the biometricmonitoring device can use motion data from a motion/inertia sensor todetermine that the user is in a condition suitable for collecting PPGdata for PWA. In other words, if the user is relatively still, thebiometric monitoring device will trigger the PPG sensor of the device tocollect pulse waveform data. In these implementations, the biometricmonitoring device includes one or more of the motion sensors describedherein.

In some implementations, the one or more conditions for triggering thePPG sensor include activity type the user is engaged in. For instance,in some implementation, the biometric monitoring device triggers PPGdata collection after determining that the user is sleeping, exercising,or working. In these implementations, the biometric monitoring devicecan use one or more of the biometric sensors described herein after todetermine that the user is sleeping. For instance, the biometricmonitoring device may use motion data, pulse waveform data, respirationrate data, etc. to determine that the user is sleeping.

In some implementations, the one or more conditions for triggering thePPG sensor include the pressure between the device and the user's bodyor user's skin contact area. The biometric monitoring device candetermine to trigger the PPG sensor when the pressure between the deviceand the user's body meets a criterion. The biometric monitoring deviceincludes a pressure sensor that can sense the pressure between the userand the device. In various implementations, the pressure sensor may beone or more of the following: a force sensor, force sensitive resistor,mechanical sensor, load sensor, load cell, strain gauge, piezo sensor,membrane potentiometer, or any other suitable pressure sensor. If thepressure is too high or too low for generating useful pulse waveformdata, the biometric monitoring device may elect to not trigger the PPGsensor to collect pulse waveform data. In some implementations, thebiometric monitoring device may present information to the userindicating that a condition for collecting PPG data for PWA is not met,so that the user may correct the condition of the device to allow PPGdata collection.

Other user conditions for triggering the PPG sensor to collect pulsewaveform data include but are not limited to: body temperature, bodyposition (e.g., arm orientation), noise in previously obtained pulsewaveform data being above a noise threshold, historical activity datameeting a past activity criterion, a placement of the PPG sensor isdetermined to be appropriate, hydration level, activities currentlyengaged in, activities recently completed, diet, moisture level, anorientation of the device meeting a criterion, a posture of the usermeeting a criterion. In some implementation, the placement of the sensoris determined to be appropriate when the biometric data indicates thatthe PPG sensor is placed on the user's body near an artery. In someimplementations, past activity criteria include but are not limited to,for instance, a lack of vigorous activity in the past X (e.g., 30)minutes, step rate in the last X (e.g., 30) minutes never exceeding astep threshold (e.g. 120 steps per minute), and the last X (e.g., 10)minutes consisting of sedentary activity.

In these implementations, the biometric monitoring device includes oneor more biometric sensors described herein after, and is configured touse biometric data obtained from the sensors to determine whether one ormore of the conditions are satisfied.

In some implementations, the biometric monitoring device can collectmotion data and record historical activity data of the user. In someimplementations, the historical activity data include the amount, timeand type of the last activity. In some implementations, the biometricmonitoring device records the time between the last activity and thetime when pulse waveform data is collected.

In some implementations, the device automatically triggers obtaining thePPG sensor data for at least about X (e.g., 3) pulse waveforms over aperiod of Y (e.g., 60) seconds. In some implementations, the deviceautomatically triggers obtaining the PPG sensor data for at least 1pulse waveform every day over a period of at least 2 days. In someimplementations, the device automatically triggers the PPG sensor tooperate at the higher sampling rate (e.g., 25 Hz or faster, 50 Hz orfaster, 100 Hz or faster, 150 Hz or faster, or 200 Hz or faster) in twoor more intervals distributed in two or more different hours in a periodof time (e.g., 24 hours). The PPG sensor operates at the lower samplingrate (e.g., slower than 25 Hz) in at least some intervals of in theperiod of times.

In some implementations, the biometric monitoring device can determinethe user's heart rate using the PPG sensor data and trigger obtainingthe PPG sensor data for a number of pulse waveforms, wherein the numberof pulse waveforms depends at least partly on the user's heart rate. Insome implementations, the number of pulse waveforms increases as theuser's heart rate increases.

In some implementations, the biometric monitoring device can determinethe user's respiration rate and trigger obtaining the PPG sensor datafor a number of pulse waveforms, wherein the number of pulse waveformsdepends at least partly on the user's respiration rate. In someimplementations, the number of pulse waveforms increases as the user'srespiration rate increases.

After the PPG sensor has collected data for one or more waveforms,process XC00 determines whether enough PPG data for PWA has beencollected. For instance, some implementations use PPG data that issufficient for obtaining at least a few high-quality pulse waveforms.For instance, some implementations collect PPG data sufficient togenerate at least about 2, 10, 20, 50, or 100 waveforms in a datacollection period. See block 208. If not, the process loops back to anearlier decision block, e.g., block 202. If enough PPG data has beencollected, the biometric monitoring device preprocesses PPG data forPWA. See block 210. After PPG data has been preprocessed, such as by aprocess 300 shown in FIG. 3, PWA is performed to obtain one or moremorphological features for determining arterial stiffness. See block212. In some implementations, the biometric monitoring device presentsthe one or more morphological features to a user. In someimplementations, the biometric monitoring device can apply the one ormore morphological features to a model to predict or estimate arterialstiffness, such as in a process shown in FIG. 5.

In some implementations, the biometric monitoring device obtainsadditional variables that are selected from the group consisting of:weight measurement (e.g., BMI), temperature, respiration measurements,blood pressure measurements, sleep stage, user height, orientation ofthe biometric monitoring device, an activity pattern recently detected,and variables derived therefrom. In some implementations, the biometricmonitoring device can apply the one or more of the additional variablesto the model to predict or estimate arterial stiffness.

In some implementations, the biometric monitoring device displaysinformation indicating the estimated arterial stiffness on the biometricmonitoring device. Such display may be visual and/or auditory.

In some implementations, one or more measurements indicating arterialstiffness are obtained from cross-correlation of pulse waveforms withpreexisting templates or decomposition of the pulse waveforms using oneor more approximation methods. In some implementations, the one or moreapproximation methods include curve fitting.

In some implementations, process 200 is implemented by biometricmonitoring device having: (a) a wearable fixing structure configured toattach to a user and/or a user's apparel in a manner allowing the userto wear the biometric monitoring device while performing activities; (b)an inertial sensor configured to generate inertial data measuringmovement experienced by the biometric monitoring device; (c) aphotoplethysmogram (“PPG”) sensor to generate PPG sensor data; and (d)one or more processors. The processors are configured to determine oneor more morphological features of a pulse waveform from PPG sensor datacollected through the PPG sensor, and/or transmit the PPG sensor data toa device configured to determine the one or more morphological featuresof the pulse waveform from the PPG sensor data, wherein the one or moremorphological features are related to arterial stiffness.

In some implementations, the processors are configured to: (i) obtainthe inertial data from the inertial sensor, (ii) obtain the PPG sensordata from the PPG sensor, (iii) filter the PPG sensor data usinginformation obtained from the inertial data, and (iv) determine one ormore morphological features of a pulse waveform derived from thefiltered PPG sensor data, and/or transmit the filtered PPG sensor datato a device configured to determine the one or more morphologicalfeatures of the pulse waveform from the filtered PPG sensor data. Insome implementations, the one or more processors are further configuredto, before (ii): determine that one or more conditions for collectingpulse waveform data from the user are satisfied; and trigger, based onthe determination that the one or more conditions are satisfied, the PPGsensor to obtain the PPG sensor data.

In some implementations, one or more activities are applied to a modelto predict or estimate arterial stiffness. The activities may includeone or more activities selected from the group consisting of exercising,working, and sleeping.

In some implementations, the one or more conditions for triggering PPGdata collection include the user's activity or location.

In some implementations, the one or more processors are configured todetermine the user's activity from the inertial data, which may beimplemented by comparing the inertial data to data of an activityprofile. In some implementations, the inertial sensor includes anaccelerometer. Activity profiles may be created for various activities,such as resistance training, aerobic exercising, endurance training,sitting, working, and sleeping.

In some implementations, the one or more processors are configured todetermine a biometric feature, e.g., a step count, of the one or moreactivities of the user.

In some implementations, the wearable fixing structure includes a strapfor attaching to the user's limb or a strap for attaching to the user'swrist. In some implementations, the strap and/or the one or more sensorsare configured to permit the PPG sensor to obtain the PPG sensor datafrom the user's ulnar and/or radial artery. For example, the strap maybe configured so that one or more PPG sensors align to the user's ulnarand/or radial artery. In some embodiments, the strap includes multiplePPG sensors, offset from one another around the length of the strap. Inthis way, a single strap design may be appropriate for many users, withsome users having a first PPG aligned with their ulnar and/or radialartery and other users having a second PPG aligned with their ulnarand/or radial artery. For example, see FIG. 13A.

In some implementations, the wearable fixing structure includes alocation sensor, a temperature sensor, a strain sensor, and/or apressure sensor. In some implementations, the one or more processors ofthe biometric monitoring device are further configured to use theinertial data to reject motion artifact.

In some implementations, the one or more processors of the biometricmonitoring device are configured to trigger obtaining the PPG sensordata at a schedule based on activity types or locations of the user.

FIG. 3 shows a process flow chart for preprocessing pulse waveform datathat can be performed by biometric monitoring device according to someimplementations. Process 300 starts by normalizing pulse waveforms bylinearly scaling individual waveforms on a time dimension (x-axis) andon an amplitude dimension (y-axis) so that the minima and maxima on eachdimension align across different waveforms. See block 302. After thisnormalization operation, each waveform has an equal duration andamplitude range. FIG. 4 shows example of multiple y and/or x normalizedpulse waveforms (gray) being combined into a more reliable pulsewaveform (black).

In some implementations, the relationships can be determined or modeledbetween the user conditions or related variables and one of a) bloodvolume, b) arterial compliance, and/or c) blood flow/perfusion. Knowingthe relationships (for example, arterial compliance non-linearlydecreases with finger skin temp), one can normalize the PWA features orthe pulse morphology through a transfer function. The normalizedfeatures can then be fed to arterial stiffness models for estimatingarterial stiffness.

Process 300 further involves normalizing pulse waveforms for one or moreuser conditions, such as activity type, temperature, and sensorpressure. See block 304. Other user conditions that may be normalizedinclude but are not limited to: orthostatic state (e.g. sitting, lyingdown, standing), time of day, day of week, hydrostatic pressure changes,average heart rate. In some implementations, to normalize for acondition variable, the relation between the condition variable andpulse waveform morphology is first established from training waveforms.Then the established relation is applied to scale a test waveform havinga specific value of a condition variable. In some implementations, thetraining waveforms are obtained from the same individual being tested.In other implementations, the training waveforms include waveformsobtained from one or more individuals different from the testedindividual. In other implementations, other methods for normalizing forconditions may be employed. For instance, scaling may be non-linear ifthere is a non-linear relationship between a factor and waveformmorphology.

In some implementations, normalizing the pulse waveform data involves:modeling a relationship between (a) one of the one or more factors, and(b) a variable related to the amplitude of the pulse waveform; applyingthe one factor of the user to the modeled relationship to obtain anormal pulse waveform or features thereof; and scaling the plurality ofpulse waveforms or features thereof based on the normal pulse waveformor features thereof. In some implementations, the variable related tothe amplitude of the pulse waveform is selected from blood volume,arterial compliance, blood flow, or blood perfusion.

Process 300 further involves removing outlier pulse waveform. In someimplementations, pulse waveforms are correlated. Then the waveformshaving correlation scores below a relative criterion are consideredoutliers, which may be removed from downstream PWA. See block 306. Insome implementations, pulse waveforms from the same individual beingtested are cross correlated. In other implementations, pulse waveformsfrom more than one individual may be cross correlated to identifyoutlier waveforms.

After outlier waveforms are removed, data of multiple pulse waveformscan be combined to provide more representative and less noisy pulsewaveform data, which can in turn provide more reliable estimates ofpulse waveform morphological features and arterial stiffness. See block308. Note that non-normalized waveforms corresponding to the non-outlierwaveforms may be aggregated as well or in place of aggregating thenormalized pulse waveforms. In some implementations, some pulse waveformmorphological features are obtained from normalized waveforms, and otherpulse waveform morphological features are obtained from un-normalizedwaveforms.

In some implementations, in addition to or instead of normalizing forthe user conditions, one or more of the user conditions may be providedas input variables to a model that relates input variables to arterialstiffness, where the model also includes input variables obtained frompulse waveform. As such, the arterial stiffness estimated by the modelaccounts for different user conditions. For instance, one may usetemperature, sensor pressure, posture (e.g., Euler angles), useractivity, etc., as parameters or features in the model (e.g., neuralnetwork, etc.). Therefore, some implementations obtain one or moremeasurements indicating arterial stiffness by applying the one or morefactors, as well as the pulse waveform data, to a model to obtain theone or more measurements

In some implementations, user conditions or related measurements can beused to identify which one of a plurality of trained models should beused to estimate arterial stiffness. For instance, one may apply onemodel for sitting and another for standing, one for very high skintemperature and one for very low temperature, etc. In suchimplementations, a decision tree may be used to decide which particularmodel to use to estimate arterial stiffness. Some implementations obtainone or more measurements indicating arterial stiffness by (a) selectinga model based on the one or more factors, and (b) applying aggregatepulse waveform data to the selected model to obtain the one or moremeasurements indicating arterial stiffness.

FIG. 5 illustrates a process for performing PWA to determine arterialstiffness. Process 500 involves determining the class for a user. Theuser class may be defined by one or more variables that co-vary with themeasurement of arterial stiffness through PWA. Among such variables, theuser class may be defined by variables that affect or covary with PPGsignal but not with the arterial stiffness, which may help to rule outfactors unrelated to arterial stiffness. For instance, the user may beplaced into a class based on BMI and age. Other variables co-varyingwith the measurement of arterial stiffness though PWA may also be usedto define the user class. Such variables include but are not limited to:gender, height, weight, ethnicity blood pressure, body fat percentage,other health or disease condition, etc. See Block 502. Process 500involves providing a pulse waveform template for the user class. SeeBlock 504. In some implementations, a user class includes individuals ina group obtained by one or more clustering techniques. For instance, oneor more clustering techniques may be applied to obtain four clusters(601-604) of individuals shown in FIG. 6. The clustering techniques mayemploy connectivity models, centroid models, distribution models,density models, group models, and graph models. The clustering algorithmmay include K-means algorithm, partitioning around medoid, hierarchicalclustering, etc. The user class template is obtained from pulsewaveforms of the individuals in the class. FIG. 6 shows four pulsewaveform templates (611-614) for the four clusters (601-604) withdifferent morphological features. In some implementations, datapreprocessing techniques described above are applied to obtain the userclass template.

Process 500 continues by obtaining morphological features of pulsewaveform of the user using the template of the user class. See Block506. In some implementations, the morphological features of the user maybe obtained as difference values between the user's waveform and thetemplate waveform, which are also referred to as comparison resultshereinafter. In other implementations, the morphological features of theuser may be normalized relative to the template, and then features areobtained from the normalized waveform.

In some implementations, the process involves determining an arterialstiffness measurement of the user. In some implementations, the processinvolves applying the comparison results to a model to obtain thearterial stiffness measurement of the user, wherein the model takes thecomparison results as inputs and provides a value of an arterialstiffness measurement as an output. See block 508. In someimplementations, the model includes a general linear model, a non-linearmodel, a neural network model, or a regression tree.

Device Configuration

This disclosure is directed at biometric monitoring devices (which mayalso be referred to herein and in any references incorporated byreference as “biometric tracking devices,” “personal health monitoringdevices,” “portable monitoring devices,” “portable biometric monitoringdevices,” “biometric monitoring devices,” or the like), which may begenerally described as wearable devices, typically of a small size, thatare designed to be worn relatively continuously by a person. When worn,such biometric monitoring devices gather data regarding activitiesperformed by the wearer or the wearer's physiological state. Such datamay include data representative of the ambient environment around thewearer or the wearer's interaction with the environment, e.g., motiondata regarding the wearer's movements, ambient light, ambient noise, airquality, etc., as well as physiological data obtained by measuringvarious physiological characteristics of the wearer, e.g., heart rate,perspiration levels, etc.

Biometric monitoring devices, as mentioned above, are typically small insize so as to be unobtrusive for the wearer. Fitbit offers severalvarieties of biometric monitoring devices that are all quite small andvery light, e.g., the Fitbit Flex™ is a wristband with an insertablebiometric monitoring device that is about 0.5″ wide by 1.3″ long by0.25″ thick. Biometric monitoring devices are typically designed to beable to be worn without discomfort for long periods of time and to notinterfere with normal daily activity.

In some cases, a biometric monitoring device may leverage other devicesexternal to the biometric monitoring device, e.g., an external pulsewaveform monitor or heart rate monitor in the form of an EKG sensor on achest strap may be used to obtain pulse waveform data or a GPS receiverin a smartphone may be used to obtain position data. In such cases, thebiometric monitoring device may communicate with these external devicesusing wired or wireless communications connections. The conceptsdisclosed and discussed herein may be applied to both stand-alonebiometric monitoring devices as well as biometric monitoring devicesthat leverage sensors or functionality provided in external devices,e.g., external sensors, sensors or functionality provided bysmartphones, etc.

In general, the concepts discussed herein may be implemented instand-alone biometric monitoring devices as well as, when appropriate,biometric monitoring devices that leverage external devices.

It is to be understood that while the concepts and discussion includedherein are presented in the context of biometric monitoring devices,these concepts may also be applied in other contexts as well if theappropriate hardware is available. For example, many modern smartphonesinclude motion sensors, such as accelerometers, that are normallyincluded in biometric monitoring devices, and the concepts discussedherein may, if appropriate hardware is available in a device, beimplemented in that device. In effect, this may be viewed as turning thesmartphone into some form of biometric monitoring device (although onethat is larger than a typical biometric monitoring device and that maynot be worn in the same manner). Such implementations are also to beunderstood to be within the scope of this disclosure.

The functionality discussed herein may be provided using a number ofdifferent approaches. For example, in some implementations a processormay be controlled by computer-executable instructions stored in memoryso as to provide functionality such as is described herein. In otherimplementations, such functionality may be provided in the form of anelectrical circuit. In yet other implementations, such functionality maybe provided by a processor or processors controlled bycomputer-executable instructions stored in a memory coupled with one ormore specially-designed electrical circuits. Various examples ofhardware that may be used to implement the concepts outlined hereininclude, but are not limited to, application specific integratedcircuits (ASICs), field-programmable gate arrays (FPGAs), andgeneral-purpose microprocessors coupled with memory that storesexecutable instructions for controlling the general-purposemicroprocessors.

Standalone biometric monitoring devices may be provided in a number ofform factors and may be designed to be worn in a variety of ways. Insome implementations, a biometric monitoring device may be designed tobe insertable into a wearable case or into multiple, different wearablecases, e.g., a wristband case, a belt-clip case, a pendant case, a caseconfigured to be attached to a piece of exercise equipment such as abicycle, etc. Such implementations are described in more detail in, forexample, U.S. patent application Ser. No. 14/029,764, filed Sep. 17,2013, which is hereby incorporated by reference for such purpose. Inother implementations, a biometric monitoring device may be designed tobe worn in only one manner, e.g., a biometric monitoring device that isintegrated into a wristband in a non-removable manner may be intended tobe worn only on a person's wrist (or perhaps ankle).

Portable biometric monitoring devices according to embodiments andimplementations described herein may have shapes and sizes adapted forcoupling to (e.g., secured to, worn, borne by, etc.) the body orclothing of a user. An example of a wearable biometric monitoring deviceis shown in FIG. 7; the example portable monitoring device may have auser interface, processor, biometric sensor(s), memory, environmentalsensor(s) and/or a wireless transceiver which may communicate with aclient and/or server. In various implementations, the biometric sensorsinclude a PPG sensor for generating PWA data.

In some implementations, the wearable biometric monitoring deviceintegrates a plurality of biometric sensors. FIG. 8 is a block diagramshowing components of a biometric monitoring device in such animplementation. The biometric monitoring device includes a processor, adisplay element, communication circuitry, and a plurality of biometricsensors that are communicatively linked and contained in a housingstructure. In some implementations, instead of enclosed in a housing,one or more of the sensors may be integrated into auxiliary structureconnected to the housing structure. For example, the PPG sensor may beintegrated into a wristband that is attached to a housing, providing adevice with a form similar to a wrist-watch that can be worn on thewrist. See FIG. 13A. The plurality of the biometric sensors in theexample illustrated in FIG. 8 includes a PPG sensor, an inertial ormotion sensor, a temperature sensor, a pressure sensor, and optionallyan altimeter.

In some implementations, the PPG sensor is configured to collect datafor deriving pulse waveforms under operational conditions as describedabove in association with pulse wave analysis. In some implementations,the PPG sensor can have additional operation modes for collecting datafor other biometric measurements such as heartbeat, skin proximity, andskin color.

The wearable biometric monitoring device illustrated here also includesan inertial sensor or a motion sensor that can be used to detect motionor the lack of motion of a user wearing the device. In someimplementations, the device can use the motion information to removemotion noise from PDG data used for PWA. In some implementations, thedevice can use the motion information to determine activities of theuser, which activity information can be used in PWA. The inertial sensorcan also be used, either alone or in combination with other sensors, theposition or orientation of the user's body part to which the device isattached (e.g., the orientation or position of the user's wrist or arm).In some implementations, such information may be used to calibrate thePPG and/or taken into account in PWA.

The wearable biometric monitoring device also includes a temperaturesensor that can be used to measure the user's skin temperature, whichcan be used to normalize PPG data or accounted for in PWA.

The wearable biometric monitoring device also includes a pressure sensorthat can be used to measure the pressure between the sensor and theuser's tissue, which can affect the features and dynamics of the pulsewave. In some implementations, the measured the pressure can be used tonormalize PPG data or accounted for in PWA. The pressure sensor mayinclude one or more of the following or combinations thereof: a forcesensor, a force sensitive resistor, a mechanical sensor, a load sensor,a load cell, a strain gauge, a piezo sensor, or a membranepotentiometer.

The wearable biometric monitoring device also includes an optionallocation sensor and an optional altimeter. These sensors are optional insome implementations so they all illustrated by dashed lines.

An example of a wrist-worn portable biometric monitoring device is shownin FIGS. 9A through 9C. This device may have a display, button(s),electronics package, and/or an attachment band. The attachment band maybe secured to the user through the use of hooks and loops (e.g.,Velcro), a clasp, and/or a band having memory of its shape, e.g.,through the use of a spring metal band. In FIG. 9B, a sensor protrusionand recess for mating a charger and/or data transmission cable can beseen. In FIG. 9C, a cross-section through the electronics package isshown. Of note are the sensor protrusion, main PCB board, and display.

Portable biometric monitoring devices may collect one or more types ofphysiological and/or environmental data from embedded sensors and/orexternal devices and communicate or relay such information to otherdevices, including devices capable of serving as an Internet-accessibledata sources, thus permitting the collected data to be viewed, forexample, using a web browser or network-based application. For example,while the user is wearing a biometric monitoring device, the biometricmonitoring device may calculate and store the user's step count usingone or more biometric sensors. The biometric monitoring device may thentransmit data representative of the user's step count to an account on aweb service (e.g., www.fitbit.com), computer, mobile phone, or healthstation where the data may be stored, processed, and visualized by theuser. Indeed, the biometric monitoring device may measure or calculate aplurality of other physiological metrics in addition to, or in place of,the user's step count. These include, but are not limited to, energyexpenditure, e.g., calorie burn, floors climbed and/or descended, heartrate, heart rate variability, heart rate recovery, location and/orheading, e.g., through GPS, GLONASS, or a similar system, elevation,ambulatory speed and/or distance traveled, swimming lap count, swimmingstroke type and count detected, bicycle distance and/or speed, bloodpressure, blood glucose, skin conduction, skin and/or body temperature,muscle state measured via electromyography, brain activity as measuredby electroencephalography, weight, body fat, caloric intake, nutritionalintake from food, medication intake, sleep periods, e.g., clock time,sleep phases, sleep quality and/or duration, pH levels, hydrationlevels, respiration rate, and other physiological metrics. The biometricmonitoring device may also measure or calculate metrics related to theenvironment around the user such as barometric pressure, weatherconditions (e.g., temperature, humidity, pollen count, air quality,rain/snow conditions, wind speed), light exposure (e.g., ambient light,UV light exposure, time and/or duration spent in darkness), noiseexposure, radiation exposure, and magnetic field. Furthermore, thebiometric monitoring device or the system collating the data streamsfrom the biometric monitoring device may calculate metrics derived fromsuch data. For example, the device or system may calculate the user'sstress and/or relaxation levels through a combination of heart ratevariability, skin conduction, noise pollution, and sleep quality. Inanother example, the device or system may determine the efficacy of amedical intervention, e.g., medication, through the combination ofmedication intake, sleep data, and/or activity data. In yet anotherexample, the biometric monitoring device or system may determine theefficacy of an allergy medication through the combination of pollendata, medication intake, sleep and/or activity data. These examples areprovided for illustration only and are not intended to be limiting orexhaustive. Further embodiments and implementations of sensor devicesmay be found in U.S. Pat. No. 9,042,971, titled “Biometric MonitoringDevice with Pulse waveform measurement or heart rate measurementActivated by a Single User Gesture” and filed on Jan. 13, 2014, U.S.Pat. No. 9,044,149, titled “Pulse waveform data Collection” and filed onMay 29, 2014, U.S. Pat. No. 8,948,832, titled “Wearable Pulse waveformmonitor or heart rate monitor” and filed on May 30, 2014, U.S. patentapplication Ser. No. 13/156,304, titled “Portable Biometric MonitoringDevices and Methods of Operating Same” filed on Jun. 8, 2011 and U.S.Patent Application 61/680,230, titled “Fitbit Tracker” filed Aug. 6,2012, which are hereby incorporated herein by reference in theirentireties.

Physiological Sensors

Biometric monitoring devices as discussed herein may use one, some orall of the following sensors to acquire physiological data, including,but not limited to, the physiological data outlined in the table below.All combinations and permutations of physiological sensors and/orphysiological data are intended to fall within the scope of thisdisclosure. Biometric monitoring devices may include but are not limitedto types of one, some, or all of the sensors specified below for theacquisition of corresponding physiological data; indeed, other type(s)of sensors may also or alternatively be employed to acquire thecorresponding physiological data, and such other types of sensors arealso intended to fall within the scope of the present disclosure.Additionally, the biometric monitoring device may derive thephysiological data from the corresponding sensor output data, but is notlimited to the number or types of physiological data that it couldderive from said sensor.

Physiological Sensors Physiological data acquired Optical ReflectometerHeart Rate, Heart Rate Variability Example Sensors: SpO₂ (Saturation ofPeripheral Oxygen) Light emitter and receiver Respiration Multi orsingle LED and photo diode Stress arrangement Blood pressure Wavelengthtuned for specific physiological Arterial Stiffness signals Bloodglucose levels Synchronous detection/amplitude modulation Blood volumeHeart rate recovery Cardiac health Motion Detector Activity leveldetection Example Sensors: Sitting/standing detection Inertial sensors,Gyroscopic sensors, and/or Fall detection Accelerometers GPS SkinTemperature Stress EMG (eletromyographic sensor) Muscle tension EKG orECG (electrocardiographic sensor) Heart Rate Example Sensors: Heart RateVariability Single-lead ECG or EKG Heart Rate Recovery Dual-lead ECG orEKG Stress Cardiac health Magnetometer Activity level based on rotationLaser Doppler Power Meter Ultrasonic Sensor Blood flow Audio SensorHeart Rate Heart Rate Variability Heart Rate Recovery Laugh detectionRespiration Respiration type, e.g., snoring, breathing, breathingproblems (such as sleep apnea) User's voice Strain gauge Heart RateExample: Heart Rate Variability In a wrist band Stress Wet/ImmersionSensor Stress Example Sensor: Swimming detection Galvanic skin responseShower detection

In one example embodiment, the biometric monitoring device may includean optical sensor to detect, sense, sample and/or generate data that maybe used to determine information representative of, for example, stress(or level thereof), blood pressure, and/or heart rate of a user. (See,for example, FIGS. 9A through 10C and 18A through KKG). In suchembodiments, the biometric monitoring device may include an opticalsensor having one or more light sources (LED, laser, etc.) to emit oroutput light into the user's body, as well as light detectors(photodiodes, phototransistors, etc.) to sample, measure and/or detect aresponse or reflection of such light from the user's body and providedata used to determine data that is representative of stress (or levelthereof), blood pressure, and/or heart rate of a user (e.g., such as byusing photoplethysmography).

In one example embodiment, a user's pulse waveform measurement or heartrate measurement may be triggered by criteria determined by one or moresensors (or processing circuitry connected to them). For instance, whendata from a motion sensor(s) indicates a period of stillness or oflittle motion, the biometric monitoring device may trigger, acquire,and/or obtain a pulse waveform measurement or heart rate measurement ordata. (See, for example, FIGS. 16, 19A, and 19B).

FIG. 19A illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, and communication circuitry which is connected to aprocessor.

FIG. 19B illustrates an example of a portable biometric monitoringdevice having a heart rate or PPG sensor, motion sensor, display,vibromotor, location sensor, altitude sensor, skin conductance/wetsensor and communication circuitry which is connected to a processor.

In one embodiment, when the motion sensor(s) indicate user activity ormotion (for example, motion that is not suitable or optimum to trigger,acquire, and/or obtain desired pulse waveform measurement or heart ratemeasurement or data (for example, data used to determine a user'sresting heart rate)), the biometric monitoring device and/or thesensor(s) employed to acquire and/or obtain a desired pulse waveformmeasurement or heart rate measurement or data may be placed in, orremain in, a low power state. Since pulse waveform measurement or heartrate measurements taken during motion may be less reliable and may becorrupted by motion artifacts, it may be desirable to decrease thefrequency with which pulse waveform data samples are collected (thusdecreasing power usage) when the biometric monitoring device is inmotion.

In another embodiment, a biometric monitoring device may employ data(for example, from one or more motion sensors) indicative of useractivity or motion to adjust or modify characteristics of triggering,acquiring, and/or obtaining desired pulse waveform measurement or heartrate measurements or data (for example, to improve robustness to motionartifact). For instance, if the biometric monitoring device receivesdata indicative of user activity or motion, the biometric monitoringdevice may adjust or modify the sampling rate and/or resolution mode ofsensors used to acquire pulse waveform data (for example, where theamount of user motion exceeds a certain threshold, the biometricmonitoring device may increase the sampling rate and/or increase thesampling resolution mode of sensors employed to acquire pulse waveformmeasurement or heart rate measurement or data.) Moreover, the biometricmonitoring device may adjust or modify the sampling rate and/orresolution mode of the motion sensor(s) during such periods of useractivity or motion (for example, periods where the amount of user motionexceeds a certain threshold). In this way, when the biometric monitoringdevice determines or detects such user activity or motion, the biometricmonitoring device may place the motion sensor(s) into a higher samplingrate and/or higher sampling resolution mode to, for example, enable moreaccurate adaptive filtering of the heart rate signal. (See, for example,FIG. 16).

FIG. 16 illustrates an example of a portable biometric monitoring devicethat changes how it detects a user's heart rate based on how muchmovement the biometric monitoring device is experiencing. In the casewhere there is motion detected (e.g., through the use of anaccelerometer), the user may be considered by the biometric monitoringdevice to be “active” and high-sampling-rate heart rate detection mayoccur to reduce motion artifacts in the pulse waveform measurement orheart rate measurement. This data may be saved and/or displayed. In thecase that the user is determined by the biometric monitoring device tonot be moving (or to be relatively sedentary), low-sampling-rate heartrate detection (which does not consume as much power) may be adequate tomeasure a heart rate and may thus be used.

Notably, where a biometric monitoring device employs optical techniquesto acquire pulse waveform measurement or heart rate measurements ordata, e.g., by using photoplethysmography, a motion signal may beemployed to determine or establish a particular approach or technique todata acquisition or measurement by the heart rate or pulse wave sensor(e.g., synchronous detection rather than a non-amplitude-modulatedapproach) and/or analysis thereof. (See, for example, FIG. 18E). In thisway, the data which is indicative of the amount of user motion oractivity may cause the biometric monitoring device to establish oradjust the type or technique of data acquisition or measurement used byan optical pulse waveform sensor or sensors.

For example, in one embodiment, a biometric monitoring device (orheart-rate measurement technique as disclosed herein may adjust and/orreduce the sampling rate of optical heart rate sampling when motiondetector circuitry detects or determines that the biometric monitoringdevice wearer's motion is below a threshold (for example, if thebiometric monitoring device determines the user is sedentary or asleep).(See, for example, FIG. 16). In this way, the biometric monitoringdevice may control its power consumption. For example, the biometricmonitoring device may reduce power consumption by reducing the sensorsampling rate—for instance, the biometric monitoring device may samplethe heart rate (via the pulse waveform sensor) once every 10 minutes, or10 seconds out of every 1 minute. Notably, the biometric monitoringdevice may, in addition thereto or in lieu thereof, control powerconsumption via controlling data processing circuitry analysis and/ordata analysis techniques in accordance with motion detection. As such,the motion of the user may impact the heart rate or pulse waveform dataacquisition parameters and/or data analysis or processing thereof.

Motion Artifact Suppression in Pulse Waveform Sensors

As discussed above, the raw heart rate signal measured by a PPG sensormay be improved by using one or more algorithms to remove motionartifacts. Movement of the user (for determining motion artifacts) maybe measured using sensors including, but not limited to, accelerometers,gyroscopes, proximity detectors, magnetometers, etc. The goal of suchalgorithms is to remove components os the PPG signal attributable tomovement (movement artifacts) using the movement signal captured fromthe other sensors as a guide. In one embodiment the movement artifactsin the PPG signal may be removed using an adaptive filter based on ahybrid Kalman filter and a least mean square filter or a recursive leastsquares filter. The heart rate may then be extracted from thecleaned/filtered signal using a peak counting algorithm or a powerspectral density estimation algorithm. Alternatively, a Kalman filter orparticle filter may be used to remove such movement artifacts.

Another approach that may be used to calculate the heart rate frequencyis to create a model of the heart rate signal as Y=Y_(dc)+Σa_(k)*coskθ+b_(k)*sin kθ, where k is the order of harmonic components, and θ is amodel parameter for heart rate. This model may then be fit to the signalusing either an extended Kalman filter or a particle filter. This modelexploits the fact that the signal is not sinusoidal so contains powerboth at the fundamental harmonic as well as multiple additionalharmonics.

Alternately, the signal may be modeled as Y=Y_(dc)+Σa_(k)*sin(k*w_(motion)t+θ)+Σb_(k)*sin(k*w_(HR)t+Ø), where w_(motion) isestimated directly from the accelerometer signal (or another motionsensor signal).

Sedentary, Sleep, and Active Classified Metrics

In yet another example embodiment, the biometric monitoring device mayemploy sensors to calculate heart rate variability when the devicedetermines the user to be sedentary or asleep. Here, the biometricmonitoring device may operate the sensors in a higher-rate sampling mode(relative to non-sedentary periods or periods of user activity thatexceed a predetermined threshold) to calculate heart rate variability.The biometric monitoring device (or an external device) may employ heartrate variability as an indicator of cardiac health or stress.

Indeed, in some embodiments, the biometric monitoring device may measureand/or determine the user's stress level and/or cardiac health when theuser is sedentary and/or asleep (for example, as detected and/ordetermined by the biometric monitoring device). Some embodiments of abiometric monitoring device of the present disclosure may determine theuser's stress level, health state (e.g., risk, onset, or progression offever or cold), and/or cardiac health using sensor data that isindicative of the heart rate variability, galvanic skin response, skintemperature, body temperature, and/or heart rate. In this way,processing circuitry of the biometric monitoring device may determineand/or track the user's “baseline” stress levels over time and/orcardiac “health” over time. In another embodiment, the device maymeasure a physiologic parameter of the user during one or more periodswhere the user is motionless (or the user's motion is below apredetermined threshold), such as when the user is sitting, lying down,asleep, or in a sleep stage (e.g., deep sleep). Such data may also beemployed by the biometric monitoring device as a “baseline” forstress-related parameters, health-related parameters (e.g., risk oronset of fever or cold), cardiac health, heart rate variability,galvanic skin response, skin temperature, body temperature and/or heartrate.

Sleep Monitoring

In some embodiments, the biometric monitoring device may automaticallydetect or determine when the user is attempting to go to sleep, isentering sleep, is asleep, and/or is awoken from a period of sleep. Insuch embodiments, the biometric monitoring device may employphysiological sensors to acquire data and the data processing circuitryof the biometric monitoring device may correlate a combination of heartrate, heart rate variability, respiration rate, galvanic skin response,motion, skin temperature, and/or body temperature data collected fromsensors of the biometric monitoring device to detect or determine if theuser is attempting to go to sleep, is entering sleep, is asleep, and/oris awoken from a period of sleep. In response, the biometric monitoringdevice may, for example, acquire physiological data (of the types, andin the manners, as described herein) and/or determine physiologicalconditions of the user (of the types, and in the manners, as describedherein). For example, a decrease or cessation of user motion combinedwith a reduction in user heart rate and/or a change in heart ratevariability may indicate that the user has fallen asleep. Subsequentchanges in heart rate variability and galvanic skin response may then beused by the biometric monitoring device to determine transitions of theuser's sleep state between two or more stages of sleep (for example,into lighter and/or deeper stages of sleep). Motion by the user and/oran elevated heart rate and/or a change in heart rate variability may beused by the biometric monitoring device to determine that the user hasawoken.

Real-time, windowed, or batch processing to maybe used to determine thetransitions between wake, sleep, and sleep stages. For instance, adecrease in heart rate may be measured in a time window where the heartrate is elevated at the start of the window and reduced in the middle(and/or end) of the window. The awake and sleep stages may be classifiedby a hidden Markov model using changes in motion signal (e.g.,decreasing motion intensity), heart rate, heart rate variability, skintemperature, galvanic skin response, and/or ambient light levels. Thetransition points may be determined through a changepoint algorithm(e.g., Bayesian changepoint analysis). The transition between awake andsleep may be determined by observing periods where the user's heart ratedecreases over a predetermined time duration by at least a certainthreshold but within a predetermined margin of the user's resting heartrate (that is observed as, for example, the minimum heart rate of theuser while sleeping). Similarly, the transition between sleep and awakemay be determined by observing an increase in the user's heart rateabove a predetermined threshold of the user's resting heart rate.

In some embodiments, the biometric monitoring device may be onecomponent of a system for monitoring sleep, where the system includes asecondary device configured to communicate with the biometric monitoringdevice and adapted to be placed near the sleeper (e.g., an alarm clock).The secondary device may, in some implementations, have a shape andmechanical and/or magnetic interface to accept the biometric monitoringdevice for safe keeping, communication, and/or charging. However, thesecondary device may also be generic to the biometric monitoring device,e.g., a smartphone that is not specifically designed to physicallyinterface with the biometric monitoring device. The communicationbetween the biometric monitoring device and the secondary device may beprovided through wired communication interfaces or through wirelesscommunication interfaces and protocols such as Bluetooth (including, forexample, Bluetooth 4.0 and Bluetooth Low Energy protocols), RFID, NFC,or WLAN. The secondary device may include sensors to assist in sleepmonitoring or environmental monitoring such as, for example, sensorsthat measure ambient light, noise and/or sound (e.g., to detectsnoring), temperature, humidity, and air quality (pollen, dust, CO2,etc.). In one embodiment, the secondary device may communicate with anexternal service such as www.fitbit.com or a server (e.g., a personalcomputer). Communication with the secondary device may be achievedthrough wired (e.g., Ethernet, USB) or wireless (e.g., WLAN, Bluetooth,RFID, NFC, cellular) circuitry and protocols to transfer data to and/orfrom the secondary device. The secondary device may also act as a relayto transfer data to and/or from the biometric monitoring device toand/or from an external service such as www.fitbit.com or other service(e.g., data such as news, social network updates, email, calendarnotifications) or server (e.g., personal computer, mobile phone,tablet). Calculation of the user's sleep data may be performed on one orboth devices or an external service (e.g., a cloud server) using datafrom one or both devices.

The secondary device may be equipped with a display to display dataobtained by the secondary device or data transferred to it by thebiometric monitoring device, the external service, or a combination ofdata from the biometric monitoring device, the secondary device, and/orthe external service. For example, the secondary device may display dataindicative of the user's heart rate, total steps for the day, activityand/or sleep goal achievement, the day's weather (measured by thesecondary device or reported for a location by an external service),etc. In another example, the secondary device may display data relatedto the ranking of the user relative to other users, such as total weeklystep count. In yet another embodiment, the biometric monitoring devicemay be equipped with a display to display data obtained by the biometricmonitoring device, the secondary device, the external service, or acombination of the three sources. In embodiments where the first deviceis equipped with a wakeup alarm (e.g., vibramotor, speaker), thesecondary device may act as a backup alarm (e.g., using an audiospeaker). The secondary device may also have an interface (e.g., displayand buttons or touch screen) to create, delete, modify, or enable alarmson the first and/or the secondary device.

Sensor-Based Standby Mode

In another embodiment, the biometric monitoring device may automaticallydetect or determine whether it is or is not attached to, disposed on,and/or being worn by a user. In response to detecting or determiningthat the biometric monitoring device is not attached to, disposed on,and/or being worn by a user, the biometric monitoring device (orselected portions thereof) may implement or be placed in a low powermode of operation—for example, the optical pulse waveform sensor and/orcircuitry may be placed in a lower power or sleep mode. For example, inone embodiment, the biometric monitoring device may include one or morelight detectors (photodiodes, phototransistors, etc.). If, at a givenlight intensity setting (for example, with respect to the light emittedby a light source that is part of the biometric monitoring device), oneor more light detectors provides a low return signal, the biometricmonitoring device may interpret the data as indicative of the device notbeing worn. Upon such a determination, the device may reduce its powerconsumption—for example, by “disabling” or adjusting the operatingconditions of the stress and/or heart rate detection sensors and/orcircuitry in addition to other device circuitry or displays (forexample, by reducing the duty cycle of or disabling the light source(s)and/or detector(s), turning off the device display, and/or disabling orattenuating associated circuitry or portions thereof). In addition, thebiometric monitoring device may periodically determine (e.g., once persecond) if the operating conditions of the stress and/or heart ratedetection sensors and/or associated circuitry should be restored to anormal operating condition (for example, light source(s), detector(s)and/or associated circuitry should return to a normal operating mode forheart rate detection). In another embodiment, the biometric monitoringdevice may restore the operating conditions of the stress and/or heartrate detection sensors and/or associated circuitry upon detection of atriggerable event—for example, upon detecting motion of the device (forexample, based on data from one or more motion sensor(s)) and/ordetecting a user input via the user interface (for example, a tap, bumpor swipe interaction with the biometric monitoring device). In somerelated embodiments, the biometric monitoring device may, for powersaving purposes, reduce its default rate of pulse waveform measurementor heart rate measurement collection to, for instance, one measurementper minute while the user is not highly active and the user may have theoption of putting the device into a mode of operation to generatemeasurements on demand or at a faster rate (e.g., once per second), forinstance, by pushing a button.

Optical Sensor(s)

In one embodiment, the optical sensors (sources and/or detectors) may bedisposed on an interior or skin-side of the biometric monitoring device(i.e., a side of the biometric monitoring device that contacts, touches,and/or faces the skin of the user (hereinafter “skin-side”). (See, forexample, FIGS. 9A through 10C). In another embodiment, the opticalsensors may be disposed on one or more sides of the device, includingthe skin-side and one or more sides of the device that face or areexposed to the ambient environment (environmental side). (See, forexample, FIGS. 13A through 14).

Optical sensors such as PPG sensors may be used to obtain data that canbe analyzed to obtain pulse waveforms or heartbeat waveform. The dataused for obtaining pulse waveform for PWA may be collected underdifferent operational mode than data used for heartbeat analysis. Forexample, PWA data in some implementations require higher samplingfrequency. Furthermore, pulse wave analysis may require morphologicalfeatures that are necessary in heartbeat analysis.

FIG. 13A illustrates an example of a portable monitoring device having aband; optical sensors and light emitters may be placed on the band.

FIG. 13B illustrates an example of a portable biometric monitoringdevice having a display and wristband. Additionally, optical PPG (e.g.,heart rate) detection sensors and/or emitters may be located on the sideof the biometric monitoring device. In one embodiment, these may belocated in side-mounted buttons.

FIG. 14 depicts a user pressing the side of a portable biometricmonitoring device to take a pulse waveform measurement or heart ratemeasurement from a side-mounted optical heart rate detection sensor. Thedisplay of the biometric monitoring device may show whether or not theheart rate has been detected and/or display the user's heart rate.

Notably, the data from such optical sensors may be representative ofphysiological data and/or environmental data. Indeed, in one embodiment,the optical sensors provide, acquire and/or detect information frommultiple sides of the biometric monitoring device whether or not thesensors are disposed on one or more of the multiple sides. For example,the optical sensors may obtain data related to the ambient lightconditions of the environment.

Where optical sensors are disposed or arranged on the skin-side of thebiometric monitoring device, in operation, a light source in thebiometric monitoring device may emit light upon the skin of the userand, in response, a light detector in the biometric monitoring devicemay sample, acquire, and/or detect corresponding reflected and/oremitted light from the skin (and from inside the body). The one or morelight sources and light detectors may be arranged in an array or patternthat enhances or optimizes the signal-to-noise ratio and/or serves toreduce or minimize power consumption by the light sources and lightdetectors. These optical sensors may sample, acquire and/or detectphysiological data which may then be processed or analyzed (for example,by resident processing circuitry) to obtain data that is representativeof, for example, a user's heart rate, respiration, heart ratevariability, oxygen saturation (SpO₂), blood volume, blood glucose, skinmoisture, and/or skin pigmentation level.

The light source(s) may emit light having one or more wavelengths thatare specific or directed to a type of physiological data to becollected. Similarly, the optical detectors may sample, measure and/ordetect one or more wavelengths that are also specific or directed to atype of physiological data to be collected and/or a physiologicalparameter (of the user) to be assessed or determined. For instance, inone embodiment, a light source emitting light having a wavelength in thegreen spectrum (for example, an LED that emits light having wavelengthscorresponding to the green spectrum) and a photodiode positioned tosample, measure, and/or detect a response or reflection correspondingwith such light may provide data that may be used to determine or detectheart rate. In contrast, a light source emitting light having awavelength in the red spectrum (for example, an LED that emits lighthaving wavelengths corresponding to the red spectrum) and a light sourceemitting light having a wavelength in the infrared spectrum (forexample, an LED that emits light having wavelengths corresponding to theIR spectrum) and photodiode positioned to sample, measure and/or detecta response or reflection of such light may provide data used todetermine or detect SpO₂.

Indeed, in some embodiments, the color or wavelength of the lightemitted by the light source, e.g., an LED (or set of LEDs), may bemodified, adjusted, and/or controlled in accordance with a predeterminedtype of physiological data being acquired or conditions of operation.Here, the wavelength of the light emitted by the light source may beadjusted and/or controlled to optimize and/or enhance the “quality” ofthe physiological data obtained and/or sampled by the detector. Forexample, the color of the light emitted by the LED may be switched frominfrared to green when the user's skin temperature or the ambienttemperature is cool in order to enhance the signal corresponding tocardiac activity. (See, for example, FIG. 18D).

The biometric monitoring device, in some embodiments, may include awindow (for example, a window that is, to casual inspection, opaque) inthe housing to facilitate optical transmission between the opticalsensors and the user. Here, the window may permit light (for example, ofa selected wavelength) to be emitted by, for example, one or more LEDs,onto the skin of the user and a response or reflection of that light topass back through the window to be sampled, measured, and/or detectedby, for example, one or more photodiodes. In one embodiment, thecircuitry related to emitting and receiving light may be disposed in theinterior of the device housing and underneath or behind a plastic orglass layer (for example, painted with infrared ink) or an infrared lensor filter that permits infrared light to pass but not light in the humanvisual spectrum. In this way, the light transmissivity of the window maybe invisible to the human eye.

The biometric monitoring device may employ light pipes or otherlight-transmissive structures to facilitate transmission of light fromthe light sources to the user's body and skin. (See, for example, FIGS.11A through 12). In this regard, in some embodiments, light may bedirected from the light source to the skin of the user through suchlight pipes or other light-transmissive structures. Scattered light fromthe user's body may be directed back to the optical circuitry in thebiometric monitoring device through the same or similar structures.Indeed, the light-transmissive structures may employ a material and/oroptical design to facilitate low light loss (for example, thelight-transmissive structures may include a lens to facilitate lightcollection, and portions of the light-transmissive structures may becoated with or adjacent to reflective materials to promote internalreflection of light within the light-transmissive structures) therebyimproving the signal-to-noise-ratio of the photo detector and/orfacilitating reduced power consumption of the light source(s) and/orlight detectors. In some embodiments, the light pipes or otherlight-transmissive structures may include a material that selectivelytransmits light having one or more specific or predetermined wavelengthswith higher efficiency than others, thereby acting as a bandpass filter.Such a bandpass filter may be tuned to improve the signal of a specificphysiological data type. For example, in one embodiment, anIn-Mold-Labeling or “IML” light-transmissive structure may beimplemented wherein the light-transmissive structure uses a materialwith predetermined or desired optical characteristics to create aspecific bandpass characteristic, for example, so as to pass infraredlight with greater efficiency than light of other wavelengths (forexample, light having a wavelength in human visible spectrum). Inanother embodiment, a biometric monitoring device may employ alight-transmissive structure having an optically opaque portion(including certain optical properties) and an optically-transparentportion (including optical properties different from theoptically-opaque portion). Such a light-transmissive structure may beprovided via a double-shot or two-step molding process wherein opticallyopaque material and optically transparent material are separatelyinjected into a mold. A biometric monitoring device implementing such alight-transmissive structure may include different light transmissivityproperties for different wavelengths depending on the direction of lighttravel through the light-transmissive structure. For example, in oneembodiment, the optically-opaque material may be reflective to aspecific wavelength range so as to more efficiently transport light fromthe user's body back to the light detector (which may be of a differentwavelength(s) relative to the wavelength(s) of the emitted light).

In another embodiment, reflective structures may be placed in the fieldof view of the light emitter(s) and/or light detector(s). For example,the sides of holes that channel light from light emitter(s) to a user'sskin and/or from the user's skin to light detector(s) (or through whichlight-transmissive structures that perform such channeling travel) maybe covered in a reflective material (e.g., chromed) to facilitate lighttransmission. The reflective material may increase the efficiency withwhich the light is transported to the skin from the light source(s) andthen from the skin back into the detector(s). The reflectively-coatedhole may be filled in with an optical epoxy or other transparentmaterial to prevent liquid from entering the device body while stillallowing light to be transmitted with low transmission loss.

In another embodiment that implements light-transmissive structures (forexample, structures created or formed through IML), suchlight-transmissive structures may include a mask consisting of an opaquematerial that limits the aperture of one, some, or all of the lightsource(s) and/or detector(s). In this way, the light-transmissivestructures may selectively “define” a preferential volume of the user'sbody that light is emitted into and/or detected from. Notably, othermask configurations may be employed or implemented in connection withthe concepts described and/or illustrated herein; all such maskingconfigurations to, for example, improve the photoplethysmography signaland which are implemented in connection with the concepts describedand/or illustrated herein are intended to fall within the scope of thepresent disclosure.

In another embodiment, the light emitter(s) and/or detector(s) may beconfigured to transmit light through a hole or series of holes in thedevice exterior. This hole or series of holes may be filled in withlight-transmissive epoxy (e.g. optical epoxy). The epoxy may form alight pipe that allows light to be transmitted from the light emitter(s)to the skin and from the skin back into the light detector(s). Thistechnique also has the advantage that the epoxy may form a watertightseal, preventing water, sweat or other liquid from entering the devicebody though the hole(s) on the device exterior that allow the lightemitter(s) and detector(s) to transmit to, and receive light from, thebiometric monitoring device body exterior. An epoxy with a high thermalconductivity may be used to help prevent the light source(s) (e.g.,LED's) from overheating.

In any of the light-transmissive structures described herein, theexposed surfaces of the optics (light-transmissive structures) or devicebody may include a hard coat paint, hard coat dip, or optical coatings(such as anti-reflection, scratch resistance, anti-fog, and/orwavelength band block (such as ultraviolet light blocking) coatings).Such characteristics or materials may improve the operation, accuracyand/or longevity of the biometric monitoring device.

FIG. 11A illustrates an example of one potential PPG light source andphotodetector geometry. In this embodiment, two light sources are placedon either side of a photodetector. These three devices are located in aprotrusion on the back of a wristband-type biometric monitoring device(the side which faces the skin of the user).

FIGS. 11B and 11C illustrate examples of a PPG sensor having aphotodetector and two LED light sources. These components are placed ina biometric monitoring device that has a protrusion on the back side.Light pipes optically connect the LEDs and photodetector with thesurface of the user's skin. Beneath the skin, the light from the lightsources scatters off of blood in the body, some of which may bescattered or reflected back into the photodetector.

FIG. 12 Illustrates an example of a biometric monitoring device with anoptimized PPG detector that has a protrusion with curved sides so as notto discomfort the user. Additionally, the surface of light pipes thatoptically couple the photodetector and the LEDs to the wearer's skin arecontoured to maximize light flux coupling between the LEDs andphotodetectors and the light pipes. The ends of the light pipes thatface the user's skin are also contoured. This contour may focus ordefocus light to optimize the PPG signal. For example, the contour mayfocus emitted light to a certain depth and location that coincides withan area where blood flow is likely to occur. The vertex of these focimay overlap or be very close together so that the photodetector receivesthe maximum possible amount of scattered light.

In some embodiments, the biometric monitoring device may include aconcave or convex shape, e.g., a lens, on the skin-side of the device,to focus light towards a specific volume at a specific depth in the skinand increase the efficiency of light collected from that point into thephotodetector. (See, for example, FIGS. 11A through 12). Where such abiometric monitoring device also employs light pipes to selectively andcontrollably route light, it may be advantageous to shape the end of thelight pipe with a degree of cylindricity, e.g., the end of the lightpipe may be a be a cylindrical surface (or portion thereof) defined by acylinder axis that is nominally parallel to the skin-side (for example,rather than use an axially-symmetric lens). For example, in awristband-style biometric monitoring device, such a cylindrical lens maybe oriented such that the cylinder axis is nominally parallel to thewearer's forearm, which may have the effect of limiting the amount oflight that enters such a lens from directions parallel to the person'sforearm and increasing the amount of light that enters such a lens fromdirections perpendicular to the person's forearm—since ambient light ismore likely to reach the sensor detection area from directions that arenot occluded by the straps of the biometric monitoring device, i.e.,along the user's forearm axis, than from directions that are occluded bythe straps, i.e., perpendicular to the user's forearm. Such aconfiguration may improve the signal-to-noise-ratio by increasing theefficiency of light transferred from the emitter onto or into the skinof the user while decreasing “stray” light from being detected orcollected by the photodetector. In this way, the signal sampled,measured and/or detected by the photodetector consists less of straylight and more of the user's skin/body response to such emitted light(signal or data that is representative of the response to the emittedlight).

In another embodiment, light-transmissive epoxy may be molded into aconcave or convex shape so as to provide beneficial optical propertiesto sensors as well. For example, during the application of lighttransmissive epoxy, the top of the light-transmissive structure that isformed by the epoxy may be shaped into a concave surface so that lightcouples more effectively into the light-transmissive structure.

In one embodiment, the components of the optical sensor may bepositioned on the skin-side of the device and arranged or positioned toreduce or minimize the distance between (i) the light source(s) and/orthe associated detector(s) and (ii) the skin of the user. See, forexample, FIG. 10A, which provides a cross-sectional view of a sensorprotrusion of an example portable monitoring device. In FIG. 10A, twolight sources (e.g., LEDs) are placed on either side of a photodetectorto enable PPG sensing. A light-blocking material is placed between thelight sources and the photodetector to prevent any light from the lightsources from reaching photodetector without first exiting the body ofthe biometric monitoring device. A flexible transparent layer may beplaced on the lower surface of the sensor protrusion to form a seal.This transparent layer may serve other functions such as preventingliquid from entering the device where the light sources orphotodetectors are placed. This transparent layer may be formed throughin-mold labeling or “IML”. The light sources and photodetector may beplaced on a flexible PCB.

Such a configuration may improve the efficiency of light flux couplingbetween the components of the optical sensor and the user's body. Forexample, in one embodiment, the light source(s) and/or associateddetector(s) may be disposed on a flexible or pliable substrate that mayflex, allowing the skin-side of the biometric monitoring device, whichmay be made from a compliant material, to conform (for example, withoutadditional processing) or be capable of being shaped (or compliant) toconform to the shape of the body part (for example, the user's wrist,arm, ankle, and/or leg) to which the biometric monitoring device iscoupled to or attached during normal operation so that the lightsource(s) and/or associated detector(s) are/is close to the skin of theuser (i.e., with little to no gap between the skin-side of the deviceand the adjacent surface of the skin of the user. See, for example, FIG.13A. In one embodiment, the light source(s) and/or associateddetector(s) may be disposed on a Flat Flex Cable or “FFC” or flexiblePCB. In this embodiment, the flexible or pliable substrate (for example,an FFC or flexible PCB) may connect to a second substrate (for example,PCB) within the device having other components disposed thereon (forexample, the data processing circuitry). Optical components of differingheights may be mounted to different “fingers” of flexible substrate andpressed or secured to the housing surface such that the opticalcomponents are flush to the housing surface. In one embodiment, thesecond substrate may be a relatively inflexible or non-pliablesubstrate, fixed within the device, having other circuitry andcomponents (passive and/or active) disposed thereon.

FIG. 10B depicts a cross-sectional view of a sensor protrusion of anexample portable monitoring device; this protrusion is similar to thatpresented in FIG. 10A with the exception that the light sources andphotodetector are placed on a flat and/or rigid PCB.

FIG. 10C provides another cross-sectional view of an example PPG sensorimplementation. Of note in this PPG sensor is the lack of a protrusion.Additionally, a liquid gasket and/or a pressure sensitive adhesive areused to prevent liquid from entering the biometric monitoring devicebody.

Some embodiments of biometric monitoring devices may be adapted to beworn or carried on the body of a user. In some embodiments including theoptical pulse waveform monitor or heart rate monitor, the device may bea wrist-worn or arm-mounted accessory such as a watch or bracelet. (See,for example, FIGS. 9A through 14). In one embodiment, optical elementsof the optical pulse waveform monitor or heart rate monitor may belocated on the interior or skin-side of the biometric monitoring device,for example, facing the top of the wrist (i.e., the optical pulsewaveform monitor or heart rate monitor may be adjacent to and facing thewrist) when the biometric monitoring device is worn on the wrist. (See,for example, FIGS. 9A through 10C).

In another embodiment, the optical pulse waveform monitor or heart ratemonitor may be located on one or more external or environmental sidesurfaces of the biometric monitoring device. (See, for example, FIGS.13B and 14). In such embodiments, the user may touch an optical window(behind which optical elements of the optical pulse waveform monitor orheart rate monitor are located) with a finger on the opposing hand toinitiate a pulse waveform measurement or heart rate measurement (and/orother metrics related to heart rate such as heart rate variability)and/or collect data which may be used to determine the user's heart rate(and/or other metrics related to heart rate). (See, for example, FIG.13B). In one embodiment, the biometric monitoring device may trigger orinitiate the measurement(s) by detecting a (sudden) drop in incidentlight on the photodiode—for example, when the user's finger is placedover the optical window. In addition thereto, or in lieu thereof, apulse waveform measurement or heart rate measurement (or other suchmetric) may be trigged by an IR-based proximity detector and/orcapacitive touch/proximity detector (which may be separate from otherdetectors). Such IR-based proximity detector and/or capacitivetouch/proximity detector may be disposed in or on and/or functionally,electrically and/or physically coupled to the optical window to detector determine the presence of, for example, the user's finger.

In yet another embodiment, the biometric monitoring device may include abutton that, when depressed, triggers or initiates pulse waveformmeasurement or heart rate measurement (and/or other metrics related toheart rate). The button may be disposed in close proximity to theoptical window to facilitate the user pressing the button while thefinger is disposed on the optical window. (See, for example, FIG. 14).In one embodiment, the optical window may be embedded in a push button.Thus, when the user presses the button, it may trigger a measurement ofthe finger that depresses the button. Indeed, the button may be given ashape and/or resistance to pressing that enhances or optimizes apressure profile of the button against the finger to provide a highsignal-to-noise-ratio during measurement or data acquisition. In otherembodiments (not illustrated), the biometric monitoring device may takethe form of a clip, a smooth object, a pendant, an anklet, a belt, etc.that is adapted to be worn on the body, clipped or mounted to an articleof clothing, deposited in clothing (e.g., in a pocket), or deposited inan accessory (e.g., handbag).

In one specific embodiment, the biometric monitoring device may includea protrusion on the skin- or interior side of the device. (See, FIGS. 9Athrough 13A). When coupled to the user, the protrusion may engage theskin with more force than the surrounding device body. In thisembodiment, an optical window or light transmissive structure (both ofwhich are discussed in detail above) may form or be incorporated in aportion of the protrusion. The light emitter(s) and/or detector(s) ofthe optical sensor may be disposed or arranged in the protrusion nearthe window or light transmissive structure. (See, for example, FIGS. 9Band 13A). As such, when attached to the user's body, the window portionof the protrusion of the biometric monitoring device may engage theuser's skin with more force than the surrounding device body—therebyproviding a more secure physical coupling between the user's skin andthe optical window. That is, the protrusion may cause sustained contactbetween the biometric monitoring device and the user's skin that mayreduce the amount of stray light measured by the photodetector, decreaserelative motion between the biometric monitoring device and the user,and/or provide improved local pressure to the user's skin; all of whichmay increase the quality of the cardiac signal of interest. Notably, theprotrusion may contain other sensors that benefit from close proximityand/or secure contact to the user's skin. These may be included inaddition to or in lieu of a pulse waveform sensor and include sensorssuch as a skin temperature sensor (e.g., noncontact thermopile thatutilizes the optical window or thermistor joined with thermal epoxy tothe outer surface of the protrusion), pulse oximeter, blood pressuresensor, EMG, or galvanic skin response (GSR) sensor.

In addition thereto, or in lieu thereof, a portion of the skin-side ofthe biometric monitoring device may include a friction enhancingmechanism or material. For example, the skin-side of the biometricmonitoring device may include a plurality of raised or depressed regionsor portions (for example, small bumps, ridges, grooves, and/or divots).Moreover, a friction enhancing material (for example, a gel-likematerial such as silicone or other elastomeric material) may be disposedon the skin-side. Indeed, a device back made out of gel may also providefriction while also improving user comfort and preventing stray lightfrom entering. As noted above, a friction-enhancing mechanism ormaterial may be used alone or in conjunction with the biometricmonitoring device having a protrusion as described herein. In thisregard, the biometric monitoring device may include a plurality ofraised or depressed regions portions (for example, small bumps, ridges,grooves, and/or divots) in or on the protrusion portion of the device.Indeed, such raised or depressed regions portions may beincorporated/embedded into or on a window portion of the protrusion. Inaddition thereto, or in lieu thereof, the protrusion portion may consistof or be coated with a friction enhancing material (for example, agel-like material such as silicone). Notably, the use of a protrusionand/or friction may improve measurement accuracy of data acquisitioncorresponding to certain parameters (e.g., heart rate, heart ratevariability, galvanic skin response, skin temperature, skin coloration,heat flux, blood pressure, blood glucose, etc.) by reducing motion ofthe biometric monitoring device (and thus of the sensor) relative to theuser's skin during operation, especially while the user is in motion.

Some or all of the interior or skin-side housing of the biometricmonitoring device may also consist of a metal material (for example,steel, stainless steel, aluminum, magnesium, or titanium). Such aconfiguration may provide a structural rigidity. (See, for example, FIG.9B). In such an embodiment, the device body may be designed to behypoallergenic through the use of a hypoallergenic “nickel-free”stainless steel. Notably, it may be advantageous to employ (at least incertain locations) a type of metal that is at least somewhat ferrous(for example, a grade of stainless steel that is ferrous). In suchembodiments, the biometric monitoring device (where it includes arechargeable energy source (for example, rechargeable battery)) mayinterconnect with a charger via a connector that secures itself to thebiometric monitoring device using magnets that couple to the ferrousmaterial. In addition, biometric monitoring device may also engage adock or dock station, using such magnetic properties, to facilitate datatransfer. Moreover, such a housing may provide enhanced electromagneticshielding that would enhance the integrity and reliability of theoptical pulse waveform sensor and the pulse waveform data acquisitionprocess/operation. Furthermore, a skin temperature sensor may bephysically and thermally coupled, for example, with thermal epoxy, tothe metal body to sense the temperature of the user. In embodimentsincluding a protrusion, the sensor may be positioned near or in theprotrusion to provide secure contact and localized thermal coupling tothe user's skin.

In a preferred embodiment, one or more components of the optical sensor(which may, in one embodiment, be located in a protrusion, and/or inanother embodiment, may be disposed or placed flush to the surface ofthe biometric monitoring device) are attached, fixed, included, and/orsecured to the biometric monitoring device via a liquid-tight seal(i.e., a method/mechanism that prevents liquid ingress into the body ofthe biometric monitoring device). For example, in one embodiment, adevice back made out of a metal such as, but not limited to, stainlesssteel, aluminum, magnesium, or titanium, or from a rigid plastic mayprovide a structure that is stiff enough to maintain the structuralintegrity of the device while accommodating a watertight seal for thesensor package. (See, for example, FIGS. 9B through 10C).

In a preferred embodiment, a package or module of the optical sensor maybe connected to the device with a pressure-sensitive adhesive and aliquid gasket. See, for example, FIG. 10C, which provides anothercross-sectional view of a PPG sensor implementation. Of note in this PPGsensor is the lack of a protrusion. Additionally, a liquid gasket and/ora pressure sensitive adhesive are used to prevent liquid from enteringthe device body. Screws, rivets or the like may also be used, forexample, if a stronger or more durable connection is required betweenthe optical sensor package/module and the device body. Notably, thepresent embodiments may also use watertight glues, hydrophobic membranessuch as Gore-Tex, o-rings, sealant, grease, or epoxy to secure or attachthe optical sensor package/module to the biometric monitoring devicebody.

As discussed above, the biometric monitoring device may include amaterial disposed on the skin- or interior side that includes highreflectivity characteristics—for example, polished stainless steel,reflective paint, and polished plastic. In this way, light scattered offthe skin-side of the device may be reflected back into the skin in orderto, for example, improve the signal-to-noise-ratio of an optical pulsewaveform sensor. Indeed, this effectively increases the input lightsignal as compared with a device body back that is non-reflective (orless reflective). Notably, in one embodiment, the color of the skin orinterior side of the biometric monitoring device may be selected toprovide certain optical characteristics (for example, reflect certain orpredetermined wavelengths of light), in order to improve the signal withrespect to certain physiological data types. For example, where theskin- or interior side of the biometric monitoring is green, themeasurements of the heart rate may be enhanced due to the preferentialemission of a wavelength of the light corresponding to the greenspectrum. Where the skin- or interior side of the biometric monitoringis red, the measurements of the SpO₂ may be enhanced due to the emissionpreferential of a wavelength of the light corresponding to the redspectrum. In one embodiment, the color of the skin- or interior side ofthe biometric monitoring may be modified, adjusted and/or controlled inaccordance with a predetermined type of physiological data beingacquired.

FIG. 18A depicts an example schematic block diagram of an optical pulsewaveform sensor where light is emitted from a light source toward theuser's skin and the reflection of such light from the skin/internal bodyof the user is sensed by a light detector, the signal from which issubsequently digitized by an analog to digital converter (ADC). Theintensity of the light source may be modified (e.g., through a lightsource intensity control module) to maintain a desirable reflectedsignal intensity. For example, the light source intensity may be reducedto avoid saturation of the output signal from the light detector. Asanother example, the light source intensity may be increased to maintainthe output signal from the light detector within a desired range ofoutput values. Notably, active control of the system may be achievedthrough linear or nonlinear control methods such asproportional-integral-derivative (PID) control, fixed step control,predictive control, neural networks, hysteresis, and the like, and mayalso employ information derived from other sensors in the device such asmotion, galvanic skin response, etc. FIG. 18A is provided forillustration and does not limit the implementation of such a system to,for instance, an ADC integrated within a MCU, or the use of a MCU forthat matter. Other possible implementations include the use of one ormore internal or external ADCs, FPGAs, ASICs, etc.

In another embodiment, system with an optical pulse waveform sensor mayincorporate the use of a sample-and-hold circuit (or equivalent) tomaintain the output of the light detector while the light source isturned off or attenuated to save power. In embodiments where relativechanges in the light detector output are of primary importance (e.g.,pulse waveform measurement or heart rate measurement), thesample-and-hold circuit may not have to maintain an accurate copy of theoutput of the light detector. In such cases, the sample-and-hold may bereduced to, for example, a diode (e.g., Schottky diode) and capacitor.The output of the sample-and-hold circuit may be presented to an analogsignal conditioning circuit (e.g., a Sallen-Key bandpass filter, levelshifter, and/or gain circuit) to condition and amplify the signal withinfrequency bands of interest (e.g., 0.1 Hz to 10 Hz for cardiac orrespiratory function), which may then be digitized by the ADC. See, forexample, FIG. 18B.

In operation, circuit topologies such as those already described herein(e.g. a sample-and-hold circuit) remove the DC and low frequencycomponents of the signal and help resolve the AC component related toheart rate and/or respiration. The embodiment may also include theanalog signal conditioning circuitry for variable gain settings that canbe controlled to provide a suitable signal (e.g., not saturated). Theperformance characteristics (e.g., slew rate and/or gain bandwidthproduct) and power consumption of the light source, light detector,and/or sample-and-hold may be significantly higher than the analogsignal conditioning circuit to enable fast duty cycling of the lightsource. In some embodiments, the power provided to the light source andlight detector may be controlled separately from the power provided tothe analog signal conditioning circuit to provide additional powersavings. Alternatively or additionally, the circuitry can usefunctionality such as an enable, disable and/or shutdown to achievepower savings. In another embodiment, the output of the light detectorand/or sample-and-hold circuit may be sampled by an ADC in addition toor in lieu of the analog signal conditioning circuit to control thelight intensity of the light source or to measure the physiologicparameters of interest when, for example, the analog signal conditioningcircuit is not yet stable after a change to the light intensity setting.Notably, because the physiologic signal of interest is typically smallrelative to the inherent resolution of the ADC, in some embodiments, thereference voltages and/or gain of the ADC may be adjusted to enhancesignal quality and/or the ADC may be oversampled. In yet anotherembodiment, the device may digitize the output of only thesample-and-hold circuit by, for example, oversampling, adjusting thereference voltages and/or gain of the ADC, or using a high resolutionADC. See, for example, FIG. 18C.

PPG DC Offset Removal Techniques

In another embodiment, the sensor device may incorporate a differentialamplifier to amplify the relative changes in the output of the lightdetector. See, for example, FIG. 18F. In some embodiments, a digitalaverage or digital low-pass filtered signal may be subtracted from theoutput of the light detector. This modified signal may then be amplifiedbefore it is digitized by the ADC. In another embodiment, an analogaverage or analog low-pass filtered signal may be subtracted from theoutput of the light detector through, for example, the use of asample-and-hold circuit and analog signal conditioning circuitry. Thepower provided to the light source, light detector, and differentialamplifier may be controlled separately from the power provided to theanalog signal conditioning circuit to improve power savings.

In another embodiment, a signal (voltage or current, depending on thespecific sensor implementation) may be subtracted from the raw PPGsignal to remove any bias in the raw PPG signal and therefore increasethe gain or amplification of the PPG signal that contains heart rate (orother circulatory parameters such as heart rate variability)information. This signal may be set to a default value in the factory,to a value based on the user's specific skin reflectivity, absorption,and/or color, and/or may change depending on feedback from an ambientlight sensor, or depending on analytics of the PPG signal itself. Forexample, if the PPG signal is determined to have a large DC offset, aconstant voltage may be subtracted from the PPG signal to remove the DCoffset and enable a larger gain, therefore improving the PPG signalquality. The DC offset in this example may result from ambient light(for example from the sun or from indoor lighting) reaching thephotodetector from or reflected light from the PPG light source.

In another embodiment, a differential amplifier may be used to measurethe difference between current and previous samples rather than themagnitude of each signal. Since the magnitude of each sample istypically much greater than the difference between each sample, a largergain can be applied to each measurement, therefore improving the PPGsignal quality. The signal may then be integrated to obtain the originaltime domain signal.

In another embodiment, the light detector module may incorporate atransimpedance amplifier stage with variable gain. Such a configurationmay avoid or minimize saturation from bright ambient light and/or brightemitted light from the light source. For example, the gain of thetransimpedance amplifier may be automatically reduced with a variableresistor and/or multiplexed set of resistors in the negative feedbackpath of the transimpedance amplifier. In some embodiments, the devicemay incorporate little to no optical shielding from ambient light byamplitude-modulating the intensity of the light source and thendemodulating the output of the light detector (e.g., synchronousdetection). See, for instance, FIG. 18E. In other aspects, if theambient light is of sufficient brightness to obtain a heart rate signal,the light source may be reduced in brightness and/or turned offcompletely.

In yet another embodiment, the aforementioned processing techniques maybe used in combination to optically measure physiological parameters ofthe user. See, for example, FIG. 18G. This topology may allow the systemto operate in a low power measurement state and circuit topology whenapplicable and adapt to a higher power measurement state and circuittopology as necessary. For instance, the system may measure thephysiologic parameter (e.g., heart rate) of interest using analogsignal-conditioning circuitry while the user is immobile or sedentary toreduce power consumption, but switch to oversampled sampling of thelight detector output directly while the user is active.

In embodiments where the biometric monitoring device includes a pulsewaveform monitor or heart rate monitor, processing of the signal toobtain pulse waveform measurement or heart rate measurements may includefiltering and/or signal conditioning such as band-pass filtering (e.g.,Butterworth filter). To counteract large transients that may occur inthe signal and/or to improve convergence of said filtering, nonlinearapproaches may be employed such as neural networks or slew ratelimiting. Data from the sensors on the device such as motion, galvanicskin response, skin temperature, etc., may be used to adjust the signalconditioning methods employed. Under certain operating conditions, theheart rate of the user may be measured by counting the number of signalpeaks within a time window or by utilizing the fundamental frequency orsecond harmonic of the signal (e.g., through a fast Fourier transform(FFT)). In other cases, such as pulse waveform data acquired while theuser is in motion, FFTs may be performed on the signal and spectralpeaks extracted, which may then be subsequently processed by amultiple-target tracker which starts, continues, merges, and deletestracks of the spectra. In some embodiments, a similar set of operationsmay be performed on the motion signal and the output may be used to doactivity discrimination (e.g., sedentary, walking, running, sleeping,lying down, sitting, biking, typing, elliptical, weight training) whichis used to assist the multiple-target tracker. For instance, it may bedetermined that the user was stationary and has begun to move. Thisinformation may be used to preferentially bias the track continuationtoward increasing frequencies. Similarly, the activity discriminator maydetermine that the user has stopped running or is running slower andthis information may be used to preferentially bias the trackcontinuation toward decreasing frequencies. Tracking may be achievedwith single-scan or multi-scan, multiple-target tracker topologies suchas joint probabilistic data association trackers, multiple-hypothesistracking, nearest neighbor, etc. Estimation and prediction in thetracker may be done through Kalman filters, spline regression, particlefilters, interacting multiple model filters, etc. A track selectormodule may use the output tracks from the multiple-spectra tracker andestimate the user's heart rate. The estimate may be taken as the maximumlikelihood track, a weight sum of the tracks against their probabilitiesof being the heart rate, etc. The activity discriminator may furthermoreinfluence the selection and/or fusion to get the heart rate estimate.For instance, if the user is sleeping, sitting, lying down, orsedentary, a prior probability may be skewed toward heart rates in the40-80 bpm range; whereas if the user is running, jogging, or doing othervigorous exercise, a prior probability may be skewed toward elevatedheart rates in the 90-180 bpm range. The influence of the activitydiscriminator may be based on the speed of the user. The estimate may beshifted toward (or wholly obtained by) the fundamental frequency of thesignal when the user is not moving. The track that corresponds to theuser's heart rate may be selected based on criteria that are indicativeof changes in activity; for instance, if the user begins to walk frombeing stationary, the track that illustrates a shift toward higherfrequency may be preferentially chosen.

The acquisition of a good heart rate signal may be indicated to the userthrough a display on the biometric monitoring device or another devicein wired or wireless communication with the biometric monitoring device(e.g., a Bluetooth Low Energy-equipped mobile phone). In someembodiments, the biometric monitoring device may include asignal-strength indicator that is represented by the pulsing of an LEDviewable by the user. The pulsing may be timed or correlated to becoincident with the user's heartbeat. The intensity, pulsing rate and/orcolor of the LED may be modified or adjusted to suggest signal strength.For example, a brighter LED intensity may represent a stronger signal orin an RGB LED configuration, a green colored LED may represent astronger signal.

In some embodiments, the strength of the heart rate signal may bedetermined by the energy (e.g., squared sum) of the signal in afrequency band of, for instance, 0.5 Hz to 4 Hz. In other embodiments,the biometric monitoring device may have a strain gauge, pressuresensor, force sensor, or other contact-indicating sensor that may beincorporated or constructed into the housing and/or in the band (inthose embodiments where the biometric monitoring device is attached toor mounted with a band like a watch, bracelet, and/or armband—which maythen be secured to the user). A signal quality metric (e.g. heart ratesignal quality) may be calculated based on data from these contactsensors either alone or in combination with data from the heart ratesignal.

In another embodiment, the biometric monitoring device may monitor heartrate optically through an array of photodetectors such as a grid ofphotodiodes or a CCD camera. Motion of the optical device with respectto the skin may be tracked through feature-tracking of the skin and/oradaptive motion correction using an accelerometer and gyroscope. Thedetector array may be in contact with the skin or offset at a smalldistance away from the skin. The detector array and its associatedoptics may be actively controlled (e.g., with a motor) to maintain astabilized image of the target and acquire a heart rate signal. Thisoptomechanical stabilization may be achieved using information frommotion sensors (e.g., a gyroscope) or image features. In one embodiment,the biometric monitoring device may implement relative motioncancellation using a coherent or incoherent light source to illuminatethe skin and a photodetector array with each photodetector associatedwith comparators for comparing the intensity between neighboringdetectors—obtaining a so-called speckle pattern which may be trackedusing a variety of image tracking techniques such as optical flow,template matching, edge tracking, etc. In this embodiment, the lightsource used for motion tracking may be different than the light sourceused in the optical pulse waveform monitor or heart rate monitor.

In another embodiment, the biometric monitoring device may consist of aplurality of photodetectors and photoemitters distributed along asurface of the device that touches the user's skin (i.e., the skin-sideof the biometric monitoring device). (See, for example, FIGS. 9A through13A). In the example of a bracelet, for instance, there may be aplurality of photodetectors and photoemitters placed at various sitesalong the circumference of the interior of the band. (See, for example,FIG. 13A). A heart rate signal-quality metric associated with each sitemay be calculated to determine the best or set of best sites forestimating the user's heart rate. Subsequently, some of the sites may bedisabled or turned off to, for example, reduce power consumption. Thedevice may periodically check the heart rate signal quality at some orall of the sites to enhance, monitor and/or optimize signal and/or powerefficiency.

In another embodiment, a biometric monitoring device may include a pulsewaveform monitor or heart rate monitoring system including a pluralityof sensors such as optical, acoustic, pressure, electrical (e.g., ECG orEKG), and motion and fuse the information from two or more of thesesensors to provide an estimate of heart rate and/or mitigate noiseinduced from motion.

In addition to pulse waveform monitor or heart rate monitoring (or otherbiometric monitoring), or in lieu thereof, the biometric monitoringdevice, in some embodiments, may include optical sensors to track ordetect time and duration of ultraviolet light exposure, total outdoorlight exposure, the type of light source and duration and intensity ofthat light source (fluorescent light exposure, incandescent bulb lightexposure, halogen, etc.), exposure to television (based on light typeand flicker rate), whether the user is indoors or outdoors, time of dayand location based on light conditions. In one embodiment, theultraviolet detection sensor may consist of a reverse biased LED emitterdriven as a light detector. The photocurrent produced by this detectormay be characterized by, for instance, measuring the time it takes forthe LED's capacitance (or alternately a parallel capacitor) todischarge.

All of the optical sensors discussed herein may be used in conjunctionwith other sensors to improve detection of the data described above orbe used to augment detection of other types of physiological orenvironmental data.

Where the biometric monitoring device includes an audio or passiveacoustic sensor, the device may contain one or more passive acousticsensors that detect sound and pressure and that can include, but are notlimited to, microphones, piezo films, etc. The acoustic sensors may bedisposed on one or more sides of the device, including the side thattouches or faces the skin (skin-side) and the sides that face theenvironment (environmental sides).

Skin-side acoustic or audio sensors may detect any type of soundtransmitted through the body and such sensors may be arranged in anarray or pattern that optimizes both the signal-to-noise-ratio and powerconsumption of such sensors. These sensors may detect respiration (e.g.,by listening to the lung), respiratory sounds (e.g., breathing, snoring)and problems (e.g., sleep apnea, etc.), heart rate (listening to theheart beat), user's voice (via sound transmitted from the vocal cordsthroughout the body).

The biometric monitoring devices of the present disclosure may alsoinclude galvanic skin-response (GSR) circuitry to measure the responseof the user's skin to emotional and physical stimuli or physiologicalchanges (e.g., the transition of sleep stage). In some embodiments, thebiometric monitoring device may be a wrist- or arm-mounted deviceincorporating a band made of conductive rubber or fabric so that thegalvanic skin response electrodes may be hidden in the band. Because thegalvanic skin response circuitry may be subjected to changingtemperatures and environmental conditions, it may also include circuitryto enable automatic calibration, such as two or more switchablereference resistors in parallel or in series with the humanskin/electrode path that allows real-time measurement of known resistorsto characterize the response of the galvanic skin response circuit. Thereference resistors may be switched into and out of the measurement pathsuch that they are measured independently and/or simultaneously with theresistance of the human skin.

Biometric Feedback

Some embodiments of biometric monitoring devices may provide feedback tothe user based on one or more biometric signals. In one embodiment, aPPG signal may be presented to the user as a real-time or near-real-timewaveform on a display of the biometric monitoring device (or on adisplay of a secondary device in communication with the biometricmonitoring device). This waveform may provide similar feedback to thewaveform displayed on an ECG or EKG machine. In addition to providingthe user with an indication of the PPG signal which may be used toestimate various heart metrics (e.g., heart rate), the waveform may alsoprovide feedback that may enable the user to optimize the position andpressure with which they are wearing the biometric monitoring device.For example, the user may see that the waveform has a low amplitude. Inresponse to this, the user may try moving the position of the biometricmonitoring device to a different location which gives a higher amplitudesignal. In some implementations, the biometric monitoring device may,based on such indications, provide instructions to the user to move oradjust the fit of the biometric monitoring device so as to improve thesignal quality.

In another embodiment, feedback about the quality of the PPG signal maybe provided to the user through a method other than displaying thewaveform. The biometric monitoring device may emit an auditory alarm(e.g. a beep) if the signal quality (e.g. signal to noise ratio) exceedsa certain threshold. The biometric monitoring device may provide avisual cue (through the use of a display for example) to the user toeither change the position of the sensor and/or increase the pressurewith which the device is being worn (for example by tightening a wriststrap in the case that the device is worn on the wrist).

Biometric feedback may be provided for sensors other than PPG sensors.For example, if the device uses ECG, EMG, or is connected to a devicewhich performs either of these, it may provide feedback to the userregarding the waveform from those sensors. If the signal-to-noise-ratioof these sensors is low, or the signal quality is otherwise compromised,the user may be instructed on how they can improve the signal. Forexample, if the heart rate cannot be detected from the ECG sensor, thedevice may provide a visual message to the user instructing them to wetor moisten the ECG electrodes to improve the signal.

Environmental Sensors

Some embodiments of biometric monitoring devices of the presentdisclosure may use one, some or all of the following environmentalsensors to, for example, acquire the environmental data, includingenvironmental data outlined in the table below. Such biometricmonitoring devices are not limited to the number or types of sensorsspecified below but may employ other sensors that acquire environmentaldata outlined in the table below. All combinations and permutations ofenvironmental sensors and/or environmental data are intended to fallwithin the scope of the present disclosure. Additionally, the device mayderive environmental data from the corresponding sensor output data, butis not limited to the types of environmental data that it could derivefrom said sensor.

Notably, embodiments of biometric monitoring devices of the presentdisclosure may use one or more, or all of the environmental sensorsdescribed herein and one or more, or all of the physiological sensorsdescribed herein. Indeed, biometric monitoring device of the presentdisclosure may acquire any or all of the environmental data andphysiological data described herein using any sensor now known or laterdeveloped—all of which are intended to fall within the scope of thepresent disclosure.

Environmental Sensors Environmental data acquired Motion DetectorLocation Potential Embodiments: Inertial, Gyroscopic or Accelerometer-based Sensors GPS Pressure/Altimeter sensor Elevation Ambient TempTemperature Light Sensor Indoor vs outdoor Watching TV (spectrum/flickerrate detection) Optical data transfer-initiation, QR codes, etc.Ultraviolet light exposure Audio Indoor vs. Outdoor Compass Locationand/or orientation Potential Embodiments: 3 Axis Compass

In one embodiment, the biometric monitoring device may include analtimeter sensor, for example, disposed or located in the interior ofthe device housing. (See, for example, FIGS. 19B and 19C; FIG. 19Cillustrates an example of a portable biometric monitoring device havingphysiological sensors, environmental sensors, and location sensorsconnected to a processor). In such a case, the device housing may have avent that allows the interior of the device to measure, detect, sampleand/or experience any changes in exterior pressure. In one embodiment,the vent may prevent water from entering the device while facilitatingmeasuring, detecting and/or sampling changes in pressure via thealtimeter sensor. For example, an exterior surface of the biometricmonitoring device may include a vent type configuration or architecture(for example, a Gore™ vent) that allows ambient air to move in and outof the housing of the device (which allows the altimeter sensor tomeasure, detect and/or sample changes in pressure), but reduces,prevents, and/or minimizes water and other liquids from flowing into thehousing of the device.

The altimeter sensor, in one embodiment, may be filled with gel thatallows the sensor to experience pressure changes outside of the gel. Thegel may act as a relatively impervious, incompressible, yet flexible,membrane that transmits external pressure variations to the altimeterwhile physically separating the altimeter (and other internalcomponents) from the outside environment. The use of a gel-filledaltimeter may give the device a higher level of environmental protectionwith or without the use of an environmentally sealed vent. The devicemay have a higher survivability rate with a gel-filled altimeter inlocations including, but not limited to, locations that have highhumidity, clothes washers, dish washers, clothes dryers, a steam room orsauna, a shower, a pool, a bath, and any location where the device maybe exposed to moisture, exposed to liquid, or submerged in liquid.

Sensors Integration/Signal Processing

Some embodiments of the biometric monitoring devices of the presentdisclosure may use data from two or more sensors to calculate thecorresponding physiological or environmental data as seen in the tablebelow (for example, data from two or more sensors may be used incombination to determine metrics such as those listed below). Thebiometric monitoring device may include, but is not limited to, thenumber, types, or combinations of sensors specified below. Additionally,such biometric monitoring devices may derive the included data from thecorresponding sensor combinations, but are not limited to the number ortypes of data that may be calculated from the corresponding sensorcombinations.

Data derived from signal processing of Sensor Integrations multiplesensors Skin Temp and Ambient Temp Heat Flux Heart Rate and MotionElevation gain Motion detector and other user's Users in the proximitymotion detector (linked by wireless communication path) Motion, anypulse waveform sensor, Sit/Standing detection galvanic skin response Anyheart rate, heart rate variability Sleep Phase detection sensor,respiration, motion Sleep Apnea detection Any pulse waveform sensorand/or Resting Heart rate wetness sensor, and/or motion Active HeartRate detector Heart rate while asleep Heart rate while sedentary Anyheart rate detector Early detection of heart problems: CardiacArrhythmia Cardiac Arrest Multiple heart rate detectors Pulse transittime Audio and/or strain gauge Typing detection GPS andphotoplethysmography Location-stress correlation: (PPG) determination ofstressful regions determination of low stress regions Activity-specificheart rate resting heart rate active heart rate Automatic activityclassification and activity heart rate determination Heart rate,galvanic skin response, User fatigue, for example while accelerometerand respiration exercising

In some embodiments, the biometric monitoring device may also include anear-field communication (NFC) receiver/transmitter to detect proximityto another device, such as a mobile phone. When the biometric monitoringdevice is brought into close or detectable proximity to the seconddevice, it may trigger the start of new functionality on the seconddevice (e.g., the launching of an “app” on the mobile phone and radiosyncing of physiological data from the device to the second device).(See, for example, FIG. 17). Indeed, the biometric monitoring device ofthe present disclosure may implement any of the circuitry and techniquesdescribed and/or illustrated in U.S. Provisional Patent Application61/606,559, filed Mar. 5, 2012, “Near Field Communication System, andMethod of Operating Same”, inventor: James Park (the contents of whichare incorporated herein by reference for such purpose).

FIG. 17 illustrates an example of a portable biometric monitoring devicethat has a bicycle application on it that may display bicycle speedand/or pedaling cadence, among other metrics. The app may be activatedwhenever the biometric monitoring device comes into proximity of apassive or active NFC tag. This NFC tag may be attached to the user'shandlebars.

In another embodiment, the biometric monitoring device may include alocation sensor (for example, GPS circuitry) and pulse waveform sensor(for example, photoplethysmography circuitry) to generate GPS- orlocation-related data and heart rate-related data, respectively. (See,for example, FIGS. 19B and 19C). The biometric monitoring device maythen fuse, process and/or combine data from these twosensors/circuitries to, for example, determine, correlate, and/or “map”geographical regions according to physiological data (for example, heartrate, stress, activity level, quantity of sleep and/or caloric intake).In this way, the biometric monitoring device may identify geographicalregions that increase or decrease a measurable user metric including,but not limited to, heart rate, stress, activity, level, quantity ofsleep and/or caloric intake.

In addition thereto, or in lieu thereof, some embodiments of biometricmonitoring devices may employ GPS-related data andphotoplethysmography-related data (notably, each of which may beconsidered data streams) to determine or correlate the user's heart rateaccording to activity levels—for example, as determined by the user'sacceleration, speed, location and/or distance traveled (as measured bythe GPS and/or determined from GPS-related data). (See, for example,FIGS. 19B and 19C). Here, in one embodiment, heart rate as a function ofspeed may be “plotted” for the user, or the data may be broken down intodifferent levels including, but not limited to, sleeping, resting,sedentary, moderately active, active, and highly active.

Indeed, some embodiments of biometric monitoring devices may alsocorrelate GPS-related data to a database of predetermined geographiclocations that have activities associated with them for a set ofpredetermined conditions. For example, activity determination andcorresponding physiological classification (for example, heart rateclassification) may include correlating a user's GPS coordinates thatcorrespond to location(s) of exercise equipment, health club and/or gymand physiological data. Under these circumstances, a user's heart rateduring, for example a gym workout, may be automatically measured anddisplayed. Notably, many physiological classifications may be based onGPS-related data including location, acceleration, altitude, distanceand/or velocity. Such a database including geographic data andphysiological data may be compiled, developed and/or stored on thebiometric monitoring device and/or external computing device. Indeed, inone embodiment, the user may create their own location database or addto or modify the location database to better classify their activities.

In another embodiment, the user may simultaneously wear multiplebiometric monitoring devices (having any of the features describedherein). The biometric monitoring devices of this embodiment maycommunicate with each other or a remote device using wired or wirelesscircuitry to calculate, for example, biometric or physiologic qualitiesor quantities that, for example, may be difficult or inaccurate tocalculate otherwise, such as pulse transit time. The use of multiplesensors may also improve the accuracy and/or precision of biometricmeasurements over the accuracy and/or precision of a single sensor. Forexample, having a biometric tracking device on the waist, wrist, andankle may improve the detection of the user taking a step over that of asingle device in only one of those locations. Signal processing may beperformed on the biometric tracking devices in a distributed orcentralized method to provide measurements improved over that of asingle device. This signal processing may also be performed remotely andcommunicated back to the biometric tracking devices after processing.

In another embodiment, heart rate or other biometric data may becorrelated to a user's food log (a log of foods ingested by a user,their nutritional content, and portions thereof). Food log entries maybe entered into the food log automatically or may be entered by the userthemselves through interaction with the biometric monitoring device (ora secondary or remote device, e.g., a smartphone, in communication withthe biometric monitoring device or some other device, e.g., a server, incommunication with the biometric monitoring device). Information may bepresented to the user regarding the biometric reaction of their body toone or more food inputs. For example, if a user has coffee, their heartrate may rise as a result of the caffeine. In another example, if a userhas a larger portion of food late at night, it may take longer for themto fall asleep than usual. Any combination of food input andcorresponding result in biometrics may be incorporated into such afeedback system.

The fusion of food intake data and biometric data may also enable someembodiments of biometric monitoring device to make an estimation of auser's glucose level. This may be particularly useful for users who havediabetes. With an algorithm which relates the glucose level to theuser's activity (e.g. walking, running, calorie burn) and nutritionalintake, a biometric monitoring device may be able to advise the userwhen they are likely to have an abnormal blood sugar level.

Processing Task Delegation

Embodiments of biometric monitoring devices may include one or moreprocessors. Figures. For example, an independent application processormay be used to store and execute applications that utilize sensor dataacquired and processed by one or more sensor processors (processor(s)that process data from physiological, environmental, and/or activitysensors). In the case where there are multiple sensors, there may alsobe multiple sensor processors. An application processor may have sensorsdirectly connected to it as well. Sensor and application processors mayexist as separate discrete chips or exist within the same packaged chip(multi-core). A device may have a single application processor, or anapplication processor and sensor processor, or a plurality ofapplication processors and sensor processors.

In one embodiment, the sensor processor may be placed on a daughterboardthat consists of all of the analog components. This board may have someof the electronics typically found on the main PCB such as, but notlimited to, transimpedance amplifiers, filtering circuits, levelshifters, sample-and-hold circuits, and a microcontroller unit. Such aconfiguration may allow the daughterboard to be connected to the mainPCB through the use of a digital connection rather than an analogconnection (in addition to any necessary power or ground connections). Adigital connection may have a variety of advantages over an analogdaughterboard to main PCB connection, including, but not limited to, areduction in noise and a reduction in the number of necessary cables.The daughterboard may be connected to the main board through the use ofa flex cable or set of wires.

Multiple applications may be stored on an application processor. Anapplication may consist of executable code and data for the application,but is not limited to these. Data may consist of graphics or otherinformation required to execute the application or it may be informationoutput generated by the application. The executable code and data forthe application may both reside on the application processor (or memoryincorporated therein) or the data for the application may be stored andretrieved from an external memory. External memory may include but isnot limited to NAND flash, NOR flash, flash on another processor, othersolid-state storage, mechanical or optical disks, RAM, etc.

The executable code for an application may also be stored in an externalmemory. When a request to execute an application is received by theapplication processor, the application processor may retrieve theexecutable code and/or data from the external storage and execute it.The executable code may be temporarily or permanently stored on thememory or storage of the application processor. This allows theapplication to be executed more quickly on the next execution request,since the step of retrieval is eliminated. When the application isrequested to be executed, the application processor may retrieve all ofthe executable code of the application or portions of the executablecode. In the latter case, only the portion of executable code requiredat that moment is retrieved. This allows applications that are largerthan the application processor's memory or storage to be executed.

The application processor may also have memory protection features toprevent applications from overwriting, corrupting, interrupting,blocking, or otherwise interfering with other applications, the sensorsystem, the application processor, or other components of the system.

Applications may be loaded onto the application processor and/or anyexternal storage via a variety of wired, wireless, optical, orcapacitive mechanisms including, but not limited to, USB, Wi-Fi,Bluetooth, Bluetooth Low Energy, NFC, RFID, Zigbee.

Applications may also be cryptographically signed with an electronicsignature. The application processor may restrict the execution ofapplications to those that have the correct signature.

Integration of Systems in a Biometric Monitoring Device

In some implementations of biometric monitoring devices, some sensors orelectronic systems in the biometric monitoring device may be integratedwith one another or may share components or resources. For example, aphotodetector for an optically-based pulse waveform sensor (such as maybe used in the heart-rate sensors discussed in U.S. Provisional PatentApplication No. 61/946,439, filed Feb. 28, 2014, and previouslyincorporated by reference herein, may also serve as a photodetector fordetermining ambient light level, such as may be used to correct for theeffects of ambient light on the pulse waveform sensor reading. Forexample, if the light source for such a heart rate detector is turnedoff, the light that is measured by the photodetector may be indicativeof the amount of ambient light that is present.

In some implementations of a biometric monitoring device, the biometricmonitoring device may be configured or communicated with using onboardoptical sensors such as the components in an optical pulse waveformmonitor or heart rate monitor. For example, the photodetectors of anoptical heart-rate sensor (or, if present, an ambient light sensor) mayalso serve as a receiver for an optically-based transmission channel,e.g., infrared communications.

In some implementations of a biometric monitoring device, a hybridantenna may be included that combines a radio frequency antenna, e.g., aBluetooth antenna or GPS antenna, with an inductive loop, such as may beused in a near-field communications (NFC) tag or in an inductivecharging system. In such implementations, the functionality for twodifferent systems may be provided in one integrated system, savingpacking volume. In such a hybrid antenna, an inductive loop may beplaced in close proximity to the radiator of an inverted-F antenna. Theinductive loop may inductively couple with the radiator, allowing theinductive loop to serve as a planar element of the antenna forradio-frequency purposes, thus forming, for example, a planar inverted-Fantenna. At the same time, the inductive loop may also serve its normalfunction, e.g., such as providing current to an NFC chip throughinductive coupling with an electromagnetic field generated by an NFCreader. Examples of such hybrid antenna systems are discussed in moredetail in U.S. Provisional Patent Application No. 61/948,470, filed Mar.5, 2014, which was previously incorporated herein by reference in the“Cross-Reference to Related Applications” section and which is againhereby incorporated by reference with respect to content directed athybrid antenna structures. Of course, such hybrid antennas may also beused in other electronic devices other than biometric monitoringdevices, and such non-biometric-monitoring-device use of hybrid antennasis contemplated as being within the scope of this disclosure.

User Interface with the Device

Some embodiments of a biometric monitoring device may includefunctionality for allowing one or more methods of interacting with thedevice either locally or remotely.

In some embodiments, the biometric monitoring device may convey datavisually through a digital display. The physical embodiment of thisdisplay may use any one or a plurality of display technologiesincluding, but not limited to one or more of LED, LCD, AMOLED, E-Ink,Sharp display technology, graphical displays, and other displaytechnologies such as TN, HTN, STN, FSTN, TFT, IPS, and OLET. Thisdisplay may show data acquired or stored locally on the device or maydisplay data acquired remotely from other devices or Internet services.The biometric monitoring device may use a sensor (for example, anAmbient Light Sensor, “ALS”) to control or adjust the amount of screenbacklighting, if backlighting is used. For example, in dark lightingsituations, the display may be dimmed to conserve battery life, whereasin bright lighting situations, the display brightness may be increasedso that it is more easily read by the user.

In another embodiment, the biometric monitoring device may use single ormulticolor LEDs to indicate a state of the device. States that thebiometric monitoring device may indicate using LEDs may include, but arenot limited to, biometric states such as heart rate or applicationstates such as an incoming message or that a goal has been reached.These states may be indicated through the LED's color, the LED being onor off (or in an intermediate intensity), pulsing (and/or rate thereof)of the LEDs, and/or a pattern of light intensities from completely offto highest brightness. In one embodiment, an LED may modulate itsintensity and/or color with the phase and frequency of the user's heartrate.

In some embodiments, the use of an E-Ink display may allow the displayto remain on without the battery drain of a non-reflective display. This“always-on” functionality may provide a pleasant user experience in thecase of, for example, a watch application where the user may simplyglance at the biometric monitoring device to see the time. The E-Inkdisplay always displays content without including the battery life ofthe device, allowing the user to see the time as they would on atraditional watch.

Some implementations of a biometric monitoring device may use a lightsuch as an LED to display the heart rate of the user by modulating theamplitude of the light emitted at the frequency of the user's heartrate. The device may depict heart rate zones (e.g., aerobic, anaerobic,etc.) through the color of an LED (e.g., green, red) or a sequence ofLEDs that light up in accordance with changes in heart rate (e.g., aprogress bar). The biometric monitoring device may be integrated orincorporated into another device or structure, for example, glasses orgoggles, or communicate with glasses or goggles to display thisinformation to the user.

Some embodiments of a biometric monitoring device may also conveyinformation to a user through the physical motion of the device. Onesuch embodiment of a method to physically move the device is the use ofa vibration-inducing motor. The device may use this method alone, or incombination with a plurality of other motion-inducing technologies.

In some implementations, a biometric monitoring device may conveyinformation to a user through audio feedback. For example, a speaker inthe biometric monitoring device may convey information through the useof audio tones, voice, songs, or other sounds.

These three information communication methods-visual, motion, andauditory—may, in various embodiments of biometric monitoring devices, beused alone or in any combination with each other or another method ofcommunication to communicate any one or plurality of the followinginformation:

-   -   That a user needs to wake up at certain time    -   That a user should wake up as they are in a certain sleep phase    -   That a user should go to sleep as it is a certain time    -   That a user should wake up as they are in a certain sleep phase        and in a preselected time window bounded by the earliest and        latest time that the user wants to wake up.    -   That an email was received    -   That the user has been inactive for a certain period of time.        Notably, this may integrate with other applications like, for        instance, a meeting calendar or sleep tracking application to        block out, reduce, or adjust the behavior of the inactivity        alert.    -   That the user has been active for a certain period of time    -   That the user has an appointment or calendar event    -   That the user has reached a certain activity metric    -   That the user has gone a certain distance    -   That the user has reached a certain mile pace    -   That the user has reached a certain speed    -   That the user has accumulated a certain elevation gain    -   That the user has taken a certain number of steps    -   That the user has had a pulse waveform measurement or heart rate        measurement recently    -   That the user's heart rate has reached a certain level    -   That the user has a normal, active, or resting heart rate of a        specific value or in a specific range    -   That the user's heart rate has enter or exited a certain goal        range or training zone    -   That the user has a new heart rate “zone” goal to reach, as in        the case of heart rate zone training for running, bicycling,        swimming, etc. activities    -   That the user has swum a lap or completed a certain number of        laps in a pool    -   An external device has information that needs to be communicated        to the user such as an incoming phone call or any one of the        above alerts    -   That the user has reached a certain fatigue goal or limit. In        one embodiment, fatigue may be determined through a combination        of heart rate, galvanic skin response, motion sensor, and/or        respiration data

These examples are provided for illustration and are not intended tolimit the scope of information that may be communicated by suchembodiments of biometric monitoring devices (for example, to the user).Note that the data used to determine whether or not an alert conditionis met may be acquired from a first device and/or one or more secondarydevices. The biometric monitoring device itself may determine whetherthe criteria or conditions for an alert have been met. Alternatively, acomputing device in communication with the biometric monitoring device(e.g., a server and/or a mobile phone) may determine when the alertshould occur. In view of this disclosure, other information that thebiometric monitoring device may communicate to the user may beenvisioned by one of ordinary skill in the art. For example, thebiometric monitoring device may communicate with the user when a goalhas been met. The criteria for meeting this goal may be based onphysiological, contextual, and environmental sensors on a first device,and/or other sensor data from one or more secondary devices. The goalmay be set by the user or may be set by the biometric monitoring deviceitself and/or another computing device in communication with thebiometric monitoring device (e.g. a server). In an example embodiment,the biometric monitoring device may vibrate when a biometric goal ismet.

Some embodiments of biometric monitoring devices of the presentdisclosure may be equipped with wireless and/or wired communicationcircuitry to display data on a secondary device in real time. Forexample, such biometric monitoring devices may be able to communicatewith a mobile phone via Bluetooth Low Energy in order to give real-timefeedback of heart rate, heart rate variability, and/or stress to theuser. Such biometric monitoring devices may coach or grant “points” forthe user to breathe in specific ways that alleviate stress (e.g. bytaking slow, deep breaths). Stress may be quantified or evaluatedthrough heart rate, heart rate variability, skin temperature, changes inmotion-activity data and/or galvanic skin response.

Some embodiments of biometric monitoring devices may receive input fromthe user through one or more local or remote input methods. One suchembodiment of local user input may use a sensor or set of sensors totranslate a user's movement into a command to the device. Such motionscould include but may not be limited to tapping, rolling the wrist,flexing one or more muscles, and swinging one's arm. Another user inputmethod may be through the use of a button such as, but not limited to,capacitive touch buttons, capacitive screen buttons, and mechanicalbuttons. In one embodiment, the user interface buttons may be made ofmetal. In embodiments where the screen uses capacitive touch detection,it may always be sampling and ready to respond to any gesture or inputwithout an intervening event such as pushing a physical button. Suchbiometric monitoring devices may also take input through the use ofaudio commands. All of these input methods may be integrated intobiometric monitoring devices locally or integrated into a remote devicethat can communicate with such biometric monitoring devices, eitherthrough a wired or wireless connection. In addition, the user may alsobe able to manipulate the biometric monitoring device through a remotedevice. In one embodiment, this remote device may have Internetconnectivity.

Alarms

In some embodiments, the biometric monitoring device of the presentdisclosure may act as a wrist-mounted vibrating alarm to silently wakethe user from sleep. Such biometric monitoring devices may track theuser's sleep quality, waking periods, sleep latency, sleep efficiency,sleep stages (e.g., deep sleep vs REM), and/or other sleep-relatedmetrics through one or a combination of heart rate, heart ratevariability, galvanic skin response, motion sensing (e.g.,accelerometer, gyroscope, magnetometer), and skin temperature. The usermay specify a desired alarm time or window of time (e.g., set alarm togo off between 7 am and 8 am). Such embodiments may use one or more ofthe sleep metrics to determine an optimal time within the alarm windowto wake the user. In one embodiment, when the vibrating alarm is active,the user may cause it to hibernate or turn off by slapping or tappingthe device (which is detected, for example, via motion sensor(s), apressure/force sensor, and/or capacitive touch sensor in the device). Inone embodiment, the device may attempt to arouse the user at an optimumpoint in the sleep cycle by starting a small vibration at a specificuser sleep stage or time prior to the alarm setting. It mayprogressively increase the intensity or noticeability of the vibrationas the user progresses toward wakefulness or toward the alarm setting.(See, for example, FIG. 15).

FIG. 15 illustrates functionality of an example portable biometricmonitoring device smart alarm feature. The biometric monitoring devicemay be able to detect or may be in communication with a device that candetect the sleep stage or state of a user (e.g., light or deep sleep).The user may set a window of time which they would like to be awoken(e.g., 6:15 am to 6:45 am). The smart alarm may be triggered by the usergoing into a light sleep state during the alarm window.

The biometric monitoring device may be configured to allow the user toselect or create an alarm vibration pattern of their choice. The usermay have the ability to “snooze” or postpone an alarm event. In oneembodiment, the user may be able to set the amount of delay for the“snooze” feature—the delay being the amount of time before the alarmwill go off again. They may also be able to set how many times thesnooze feature may be activated per alarm cycle. For example, a user maychoose a snooze delay of 5 minutes and a maximum sequential snoozenumber to be 3. Therefore, they can press snooze up to 3 times to delaythe alarm by 5 minutes each time they press snooze to delay the alarm.In such embodiments, the snooze function will not turn off the alarm ifthe user attempts to press snooze a fourth time.

Some biometric monitoring devices may have information about the user'scalendar and/or schedule. The user's calendar information may be entereddirectly into the biometric monitoring device or it may be downloadedfrom a different device (e.g. a smartphone). This information may beused to automatically set alarms or alarm characteristics. For example,if a user has a meeting at 9 am in the morning, the biometric monitoringdevice may automatically wake the user up at 7:30 am to allow the userenough time to prepare for and/or get to the meeting. The biometricmonitoring device may determine the amount of time required for the userto prepare for the meeting based on the user's current location, thelocation of the meeting, and the amount of time it would take to get thelocation of the meeting from the user's current location. Alternatively,historical data about how long the user takes to get to the meetinglocation and/or prepare to leave for the meeting (e.g. how long it takesto wake up, take a shower, have breakfast, etc. in the morning) may beused to determine at what time to wake the user. A similar functionalitymay be used for calendar events other than meetings such as eatingtimes, sleeping times, napping times, and exercise times.

In some embodiments, the biometric monitoring device may use informationon when the user went to sleep to determine when an alarm should go offto wake the user. This information may supplement calendar informationdescribed herein. The user may have a goal of approximately how manyhours of sleep they would like to get each night or week. The biometricmonitoring device may set the morning alarm at the appropriate time forthe user to meet these sleep goals. In addition to amount of time thatthe user would like to sleep each night, other sleep goals that the usermay set may include, but are not limited to, the amount of deep sleep,REM sleep, and light sleep that the user experiences while sleeping, allof which may be used by the biometric monitoring device to determinewhen to set an alarm in the morning. Additionally, the user may bealerted at night when they should go to bed to meet their sleep goals.Additionally, the user may be alerted during the day when they shouldtake a nap to meet their sleep goals. The time at which to alert a userthat they should take a nap may be determined by factors that optimizethe user's sleep quality during the nap, subsequent naps, or night-timesleep. For example, the user is likely to have a hard time fallingasleep at night if they took a nap in the early evening. The user mayalso be advised to eat certain foods or drinks or avoid certain foods ordrinks to optimize their sleep quality. For example, a user may bediscouraged from drinking alcohol close to their bed time as it islikely to decrease their sleep quality. The user may also be advised toperform certain activities or avoid certain activities to optimize theirsleep quality. For example, a user may be encouraged to exercise in theearly afternoon to improve their sleep quality. A user may bediscouraged from exercising or watching TV close to their bedtime toimprove their sleep quality.

User Interface with a Secondary Device

In some embodiments, the biometric monitoring device may transmit andreceive data and/or commands to and/or from a secondary electronicdevice. The secondary electronic device may be in direct or indirectcommunication with the biometric monitoring device. Direct communicationrefers herein to the transmission of data between a first device and asecondary device without any intermediary devices. For example, twodevices may communicate to one another over a wireless connection (e.g.Bluetooth) or a wired connection (e.g. USB). Indirect communicationrefers to the transmission of data between a first device and asecondary device with the aid of one or multiple intermediary thirddevices which relay the data. Third devices may include, but are notlimited to, a wireless repeater (e.g. WiFi repeater), a computing devicesuch as a smartphone, laptop, desktop or tablet computer, a cell phonetower, a computer server, and other networking electronics. For example,a biometric device may send data to a smartphone which forwards the datathrough a cellular network data connection to a server which isconnected through the internet to the cellular network.

In some embodiments, the secondary device that acts as a user interfaceto the biometric monitoring device may consist of a smartphone. An appon the smart phone may facilitate and/or enable the smartphone to act asa user interface to the biometric monitoring device. The biometricmonitoring device may send biometric and other data to the smartphone inreal-time or with some delay. The smartphone may send a command orcommands to the biometric monitoring device, for example, to instruct itto send biometric and other data to the smartphone in real-time or withsome delay. For example, if the user enters a mode in the app fortracking a run, the smartphone may send a command to the biometricdevice to instruct it to send data in real-time. Therefore, the user cantrack their run on their app as they go along without any delay.

Such a smartphone may have one or multiple apps to enable the user toview data from their biometric device or devices. The app may, bydefault, open to a “dashboard” page when the user launches or opens theapp. On this page, summaries of data totals such as the total number ofsteps, floors climbed miles traveled, calories burned, calories consumedand water consumed may be shown. Other pertinent information such as thelast time the app received data from the biometric monitoring device,metrics regarding the previous night's sleep (e.g. when the user went tosleep, woke up, and how long they slept for), and how many calories theuser can eat in the day to maintain their caloric goals (e.g. a caloriedeficit goal to enable weight loss) may also be shown. The user may beable to choose which of these and other metrics are shown on thedashboard screen. The user may be able to see these and other metrics onthe dashboard for previous days. They may be able to access previousdays by pressing a button or icon on a touchscreen. Alternatively,gestures such as swiping to the left or right may enable the user tonavigate through current and previous metrics.

The smartphone app may also have another page which provides a summaryof the user's activities. Activities may include, but are not limitedto, walking, running, biking, cooking, sitting, working, swimming,working out, weightlifting, commuting, and yoga. Metrics pertinent tothese activities may be presented on this page. For example, a bar graphmay show how the number of steps the user took for different portions ofthe day (e.g. how many steps every 5 minutes or 1 hour). In anotherexample, the amount of time the user spent performing a certain activityand how many calories were burned in this period of time may bedisplayed. Similar to the dashboard page, the app may providenavigational functionality to allow the user to see these and othermetrics for past days. Other time periods such as an hour, minute, week,month or year may also be selected by the user to enable them to viewtrends and metrics of their activities over shorter or larger spans oftime.

The smartphone app may also have an interface to log food that has been,or will be, eaten by the user. This interface may have a keyword searchfeature to allow the user to quickly find the food that they would liketo enter into their log. As an alternative to, or in addition to,searching for foods, users may have the ability to find a food to log bynavigating through a menu or series of menus. For example, a user maychoose the following series ofcategories—breakfast/cereal/healthy/oatmeal to arrive at the food whichthey would like to log (e.g., apple-flavored oatmeal). At any one ofthese menus, the user may be able to perform a keyword search. Forexample, the user may search for “oatmeal” after having selected thecategory “breakfast” to search for the keyword “oatmeal” within thecategory of breakfast foods. After having selected the food that theywould like to log, the user may be able to modify or enter the servingsize and nutritional content. After having logged at least one food, theapp may display a summary of the foods that were logged in a certaintime period (e.g. a day) and the nutritional content of the foods(individual and total calorie content, vitamin content, sugar content,etc.).

The smartphone app may also have a page that displays metrics regardingthe user's body such as the user's weight, body fat percentage, BMI, andwaist size. It may display a graph or graphs showing the trend of one ormultiple of these metrics over a certain period of time (e.g., twoweeks). The user may be able to choose the value of this period of timeand view previous time periods (e.g., last month).

The smartphone app may also a page which allows the user to enter howmuch water the user has consumed. Each time the user drinks some water,they may enter that amount in the unit of their choice (e.g., ozs.,cups, etc.). The app may display the total of all of the water the userhas logged within a certain time period (e.g., a day). The app may allowthe user to see previously-logged water entries and daily totals forprevious days as well as the current day.

The smartphone app may also have a page that displays online friends ofthe user. This “friends” page may enable the user to add or request newfriends (e.g., by searching for their name or by their email address).This page may also display a leaderboard of the user and his or herfriends. The user and his or friends may be ranked based on one or moremetrics. For example, the user and his or her friends may be rankedusing the total of the past seven days' step counts.

The smartphone app may also have a page that shows metrics regarding theuser's sleep for the previous night and/or previous nights. This pagemay also enable the user to log when they slept in the past byspecifying when they went to bed and when they woke. The user may alsohave the ability to enter a subjective metric about their sleep (e.g.,bad night's rest, good night's rest, excellent night's rest, etc.). Theuser may be able to view these metrics for days or time periods (e.g.,two weeks) in the past. For example, the sleep page may default toshowing a bar graph of the amount of time the user slept each night inthe last two weeks. The user may be able to also view a bar graph of theamount of time the user slept each night in the last month.

The user may also be able to access the full capabilities of thesmartphone app described herein (e.g., the ability to enter food logs,view dashboard, etc.) through an alternative or additional interface. Inone embodiment, this alternative interface may consist of a webpage thatis hosted by a server in indirect communication with the biometricmonitoring device. The webpage may be accessed through any internetconnected device using a program such as a web browser.

Wireless Connectivity and Data Transmission

Some embodiments of biometric monitoring devices of the presentdisclosure may include a means of wireless communication to transmit andreceive information from the Internet and/or other devices. The wirelesscommunication may consist of one or more interfaces such as Bluetooth,ANT, WLAN, power-line networking, and cell phone networks. These areprovided as examples and should not be understood to exclude otherexisting wireless communication methods or protocols, or wirelesscommunications techniques or protocols that are yet to be invented.

The wireless connection may be bi-directional. The biometric monitoringdevice may transmit, communicate and/or push its data to other devices,e.g., smart phones, computers, etc., and/or the Internet, e.g., webservers and the like. The biometric monitoring device may also receive,request and/or pull data from other devices and/or the Internet.

The biometric monitoring device may act as a relay to providecommunication for other devices to each other or to the Internet. Forexample, the biometric monitoring device may connect to the Internet viaWLAN but also be equipped with an ANT radio. An ANT device maycommunicate with the biometric monitoring device to transmit its data tothe Internet through the biometric monitoring device's WLAN (and viceversa). As another example, the biometric monitoring device may beequipped with Bluetooth. If a Bluetooth-enabled smart phone comes withinrange of the biometric monitoring device, the biometric monitoringdevice may transmit data to, or receive data from, the Internet throughthe smart phone's cell phone network. Data from another device may alsobe transmitted to the biometric monitoring device and stored (or viceversa) or transmitted at a later time.

Embodiments of biometric monitoring devices of the present disclosuremay also include functionality for streaming or transmitting web contentfor display on the biometric monitoring device. The following aretypical examples of such content:

-   -   1. Historical graphs of heart rate and/or other data measured by        the device but stored remotely    -   2. Historical graphs of user activity and/or foods consumed        and/or sleep data that are measured by other devices and/or        stored remotely (e.g., such as at a website like fitbit.com)    -   3. Historical graphs of other user-tracked data that are stored        remotely. Examples include heart rate, blood pressure, arterial        stiffness, blood glucose levels, cholesterol, duration of TV        watching, duration of video game play, mood, etc.    -   4. Coaching and/or dieting data based on one or more of the        user's heart rate, current weight, weight goals, food intake,        activity, sleep, and other data.    -   5. User progress toward heart rate, weight, activity, sleep,        and/or other goals.    -   6. Summary statistics, graphics, badges, and/or metrics (e.g.,        “grades”) to describe the aforementioned data    -   7. Comparisons between the aforementioned data for the user and        similar data for his/her “friends” with similar devices and/or        tracking methods    -   8. Social content such as Twitter feeds, instant messaging,        and/or Facebook updates    -   9. Other online content such as newspaper articles, horoscopes,        weather reports, RSS feeds, comics, crossword puzzles,        classified advertisements, stock reports, and websites    -   10. Email messages and calendar schedules

Content may be delivered to the biometric monitoring device according todifferent contexts. For instance, in the morning, news and weatherreports may be displayed along with the user's sleep data from theprevious night. In the evening, a daily summary of the day's activitiesmay be displayed.

Various embodiments of biometric monitoring devices as disclosed hereinmay also include NFC, RFID, or other short-range wireless communicationcircuitry that may be used to initiate functionality in other devices.For instance, a biometric monitoring device may be equipped with an NFCantenna so that when a user puts it into close proximity with a mobilephone, an app is launched automatically on the mobile phone.

These examples are provided for illustration and are not intended tolimit the scope of data that may be transmitted, received, or displayedby the device, nor any intermediate processing that may occur duringsuch transfer and display. In view of this disclosure/application, manyother examples of data that may be streamed to or via a biometricmonitoring device may be envisioned by one reasonably skilled in theart.

Charging and Data Transmission

Some embodiments of biometric monitoring devices may use a wiredconnection to charge an internal rechargeable battery and/or transferdata to a host device such as a laptop or mobile phone. In oneembodiment, similar to one discussed earlier in this disclosure, thebiometric monitoring device may use magnets to help the user align thebiometric monitoring device to a dock or cable. The magnetic field ofmagnets in the dock or cable and the magnets in the device itself may bestrategically oriented so as to force the biometric monitoring device toself-align with the dock or cable (or, more specifically, a connector onthe cable) and so as to provide a force that holds the biometricmonitoring device in the dock or to the cable. The magnets may also beused as conductive contacts for charging or data transmission purposes.In another embodiment, a permanent magnet may only be used in the dockor cable side and not in the biometric monitoring device itself. Thismay improve the performance of the biometric monitoring device where thebiometric monitoring device employs a magnetometer. If there is a magnetin the biometric monitoring device, the strong field of a nearbypermanent magnet may make it significantly more difficult for themagnetometer to accurately measure the earth's magnetic field. In suchembodiments, the biometric monitoring device may utilize a ferrousmaterial in place of a magnet, and the magnets on the dock or cable sidemay attach to the ferrous material.

In another embodiment, the biometric monitoring device may contain oneor more electromagnets in the biometric monitoring device body. Thecharger or dock for charging and data transmission may also contain anelectromagnet and/or a permanent magnet. The biometric monitoring devicecould only turn on its electromagnet when it is close to the charger ordock. The biometric monitoring device may detect proximity to the dockor charger by looking for the magnetic field signature of a permanentmagnet in the charger or dock using a magnetometer. Alternatively, thebiometric monitoring device may detect proximity to the charger bymeasuring the Received Signal Strength Indication (RSSI) of a wirelesssignal from the charger or dock, or, in some embodiments, by recognizingan NFC or RFID tag associated with the charger or dock. Theelectromagnet could be reversed, creating a force that repels the devicefrom the charging cable or dock either when the device doesn't need tobe charged, synced, or when it has completed syncing or charging. Insome embodiments, the charger or dock may include the electromagnet andmay be configured (e.g., a processor in the charger or dock may beconfigured via program instructions) to turn the electromagnet on when abiometric monitoring device is connected for charging (the electromagnetmay normally be left on such that a biometric monitoring device that isplaced on the charger is drawn against the charger by the electromagnet,or the electromagnet may be left off until the charger determines that abiometric monitoring device has been placed on the charger, e.g.,through completion of a charging circuit, recognition of an NFC tag inthe biometric monitoring device, etc., and then turned on to draw thebiometric monitoring device against the charger. Upon completion ofcharging (or of data transfer, if the charger is actually a datatransfer cradle or a combined charger/data transfer cradle), theelectromagnet may be turned off (either temporarily or until thebiometric monitoring device is again detected as being placed on thecharger) and the biometric monitoring device may stop being drawnagainst the charger. In such embodiments, it may be desirable to orientthe interface between the biometric monitoring device and the chargersuch that, in the absence of a magnetic force generated by theelectromagnet, the biometric monitoring device would fall off of thecharger or otherwise shift into a visibly different position from thecharging position (to visually indicate to a user that charging or datatransfer is complete).

Sensor Use in Data Transfer

In some implementations, biometric monitoring devices may include acommunications interface that may switch between two or more protocolsthat have different data transmission rates and different powerconsumption rates. Such switching may be driven by data obtained fromvarious sensors of the biometric monitoring device. For example, ifBluetooth is used, the communications interface may switch between usingBluetooth base rate/enhanced data rate (BR/EDR) and Bluetooth low energy(BLE) protocols responsive to determinations made based on data from thesensors of the biometric monitoring device. For example, thelower-power, slower BLE protocol may be used when sensor data fromaccelerometers in a biometric monitoring device indicates that thewearer is asleep or otherwise sedentary. By contrast, the higher-power,faster BR/EDR protocol may be used when sensor data from theaccelerometers in a biometric monitoring device indicates that thewearer is walking around. Such adaptive data transmission techniques andfunctionality are discussed further in U.S. Provisional PatentApplication No. 61/948,468, filed Mar. 5, 2014, which was previouslyincorporated herein by reference in the “Cross-Reference to RelatedApplications” section and which is again hereby incorporated byreference with respect to content directed at adaptive data transferrates in biometric monitoring devices.

Such communication interfaces may also serve as a form of sensor for abiometric monitoring device. For example, a wireless communicationsinterface may allow a biometric monitoring device to determine thenumber and type of devices that are within range of the wirelesscommunications interface. Such data may be used to determine if thebiometric monitoring device is in a particular context, e.g., indoors,in a car, etc., and to change its behavior in various ways in responseto such a determination. For example, as discussed in U.S. ProvisionalPatent Application No. 61/948,468 (incorporated by reference above),such contexts may be used to drive the selection of a particularwireless communications protocol to use for wireless communications.

Configurable App Functionality

In some embodiments, biometric monitoring devices of the presentdisclosure may include a watch-like form factor and/or a bracelet,armlet, or anklet form factor and may be programmed with “apps” thatprovide specific functionality and/or display specific information. Appsmay be launched or closed by a variety of means including, but notlimited to, pressing a button, using a capacitive touch sensor,performing a gesture that is detected by an accelerometer, moving to aspecific location or area detected by a GPS or motion sensor,compressing the biometric monitoring device body (thereby creating apressure signal inside the device that may be detected by an altimeterinside the biometric monitoring device), or placing the biometricmonitoring device close to an NFC tag that is associated with an app orset of apps. Apps may also be automatically triggered to launch or closeby certain environmental or physiological conditions including, but notlimited to, detection of a high heart rate, detection of water using awet sensor (to launch a swimming application, for example), a certaintime of day (to launch a sleep tracking application at night, forexample), a change in pressure and motion characteristic of a planetaking off or landing to launch and close an “airplane” mode app. Appsmay also be launched or closed by meeting multiple conditionssimultaneously. For example, if an accelerometer detects that a user isrunning and the user presses a button, the biometric monitoring devicemay launch a pedometer application, an altimeter data collectionapplication, and/or display. In another case where the accelerometerdetects swimming and the user presses the same button, it may launch aswimming lap-counting application.

In some embodiments, the biometric monitoring device may have aswim-tracking mode that may be launched by starting a swimming app. Inthis mode, the biometric monitoring device's motion sensors and/ormagnetometer may be used to detect swim strokes, classify swim stroketypes, detect swimming laps, and other related metrics such as strokeefficiency, lap time, speed, distance, and calorie burn. Directionalchanges indicated by the magnetometer may be used to detect a diversityof lap turn methods. In a preferred embodiment, data from a motionsensor and/or pressure sensor may be used to detect strokes.

In another embodiment, a bicycling app may be launched by moving thebiometric monitoring device within proximity of an NFC or RFID tag thatis located on the bicycle, on a mount on the bicycle, or in a locationassociated with a bicycle including, but not limited to, a bike rack orbike storage facility. (See, for example, FIG. 17). The app launched mayuse a different algorithm than is normally used to determine metricsincluding, but not limited to, calories burned, distance travelled, andelevation gained. The app may also be launched when a wireless bikesensor is detected including, but not limited to, a wheel sensor, GPS,cadence sensor, or power meter. The biometric monitoring device may thendisplay and/or record data from the wireless bike sensor or bikesensors.

Additional apps include, but are not limited to, a programmable orcustomizable watch face, stop watch, music player controller (e.g., mp3player remote control), text message and/or email display or notifier,navigational compass, bicycle computer display (when communicating witha separate or integrated GPS device, wheel sensor, or power meter),weight-lifting tracker, sit-up reps tracker, pull up reps tracker,resistance training form/workout tracker, golf swing analyzer, tennis(or other racquet sport) swing/serve analyzer, tennis game swingdetector, baseball swing analyzer, ball throw analyzer (e.g., football,baseball), organized sports activity intensity tracker (e.g., football,baseball, basketball, volleyball, soccer), disk throw analyzer, foodbite detector, typing analyzer, tilt sensor, sleep quality tracker,alarm clock, stress meter, stress/relaxation biofeedback game (e.g.,potentially in combination with a mobile phone that provides auditoryand/or visual cues to train user breathing in relaxation exercises),teeth brushing tracker, eating rate tracker (e.g., to count or track therate and duration by which a utensil is brought to the mouth for foodintake), intoxication or suitability to drive a motor vehicle indicator(e.g., through heart rate, heart rate variability, galvanic skinresponse, gait analysis, puzzle solving, and the like), allergy tracker(e.g., using galvanic skin response, heart rate, skin temperature,pollen sensing and the like (possibly in combination with externalseasonal allergen tracking from, for instance, the internet and possiblydetermining the user's response to particular forms of allergen, e.g.,tree pollen, and alerting the user to the presence of such allergens,e.g., from seasonal information, pollen tracking databases, or localenvironmental sensors in the biometric monitoring device or employed bythe user), fever tracker (e.g., measuring the risk, onset, or progressof a fever, cold, or other illness, possibly in combination withseasonal data, disease databases, user location, and/or user providedfeedback to assess the spread of a particular disease (e.g., flu) inrelation to a user, and possibly prescribing or suggesting theabstinence of work or activity in response), electronic games, caffeineaffect tracker (e.g., monitoring the physiologic response such as heartrate, heart rate variability, galvanic skin response, skin temperature,blood pressure, stress, sleep, and/or activity in either short term orlong term response to the intake or abstinence of coffee, tea, energydrinks and/or other caffeinated beverages), drug affect tracker (e.g.,similar to the previously mentioned caffeine tracker but in relation toother interventions, whether they be medical or lifestyle drugs such asalcohol, tobacco, etc.), endurance sport coach (e.g., recommending orprescribing the intensity, duration, or profile of arunning/bicycling/swimming workout, or suggesting the abstinence ordelay of a workout, in accordance with a user specified goal such as amarathon, triathlon, or custom goal utilizing data from, for instance,historical exercise activity (e.g., distance run, pace), heart rate,heart rate variability, health/sickness/stress/fever state), weightand/or body composition, blood pressure, blood glucose, food intake orcaloric balance tracker (e.g., notifying the user how many calories hemay consume to maintain or achieve a weight), pedometer, and nail bitingdetector. In some cases, the apps may rely solely on the processingpower and sensors of the present disclosure. In other cases, the appsmay fuse or merely display information from an external device or set ofexternal devices including, but not limited to, a heart rate strap, GPSdistance tracker, body composition scale, blood pressure monitor, bloodglucose monitor, watch, smart watch, mobile communication device such asa smart phone or tablet, or server.

In one embodiment, the biometric monitoring device may control a musicplayer on a secondary device. Aspects of the music player that may becontrolled include, but are not limited to, the volume, selection oftracks and/or playlists, skipping forward or backward, fast forwardingor rewinding of tracks, the tempo of the track, and the music playerequalizer. Control of the music player may be via user input orautomatic based on physiological, environmental, or contextual data. Forexample, a user may be able to select and play a track on their smartphone by selecting the track through a user interface on the biometricmonitoring device. In another example, the biometric monitoring devicemay automatically choose an appropriate track based on the activitylevel of the user (the activity level being calculated from biometricmonitoring device sensor data). This may be used to help motivate a userto maintain a certain activity level. For example, if a user goes on arun and wants to keep their heart rate in a certain range, the biometricmonitoring device may play an upbeat or higher tempo track if theirheart rate is below the range which they are aiming for.

Automated Functions Triggered by User'S Activity

Sleep Stage Triggered Functionality

Sleep stages can be monitored through various biometric signals andmethods disclosed herein, such as heart rate, heart rate variability,body temperature, body motions, ambient light intensity, ambient noiselevel, etc. Such biometrics may be measured using optical sensors,motion sensors (accelerometers, gyroscopic sensors, etc.), microphones,and thermometers, for example, as well as other sensors discussedherein.

The biometric monitoring device may have a communication module as well,including, but not limited to, Wi-Fi (802.xx), Bluetooth (Classic, lowpower), or NFC. Once the sleep stages are estimated, the sleep stagesmay be transmitted to a cloud-based system, home server, or main controlunit that is connected to communication-enabled appliances (with Wi-Fi,Bluetooth, or NFC) wirelessly. Alternatively, the biometric monitoringdevice may communicate directly with the communication-enabledappliances. Such communication-enabled appliances may include, forexample, kitchen appliances such as microwaves, ovens, coffeegrinders/makers, toasters, etc.

Once the sleep stages indicate that it is close the time for the user towake up, the biometric monitoring device may send out a trigger to theappliances that the user has indicated should be operated automatically.For example, the coffee grinder and maker may be caused to start makingcoffee, and the toaster may be caused to start warming up bread. Themicrowave oven may be caused to start cooking oatmeal or eggs as well,and electric kettle to start boiling water. So long as the ingredientsare appropriately prepared, this automated signal may triggerbreakfast-cooking.

Alertness Detection

Alertness, e.g., a low alertness may correlate with a person beingdrowsy, may also be detected from the biometrics listed above, and maybe used to trigger an appliance such as a coffee maker to start brewingcoffee automatically.

Hydration

The portable biometric monitoring device in combination with an activitylevel tracker may submit the user's activity level to a cloud-basedsystem, home server, main control unit, or appliances directly. This maytrigger some actions of the appliances, especially related to hydration,such as starting the ice cube maker of a refrigerator, or loweringoperating temperature of a water purifier.

Power Saving

Many appliances typically operate in a low-power idle state thatconsumes power. Using aggregated information of the user's biometricsignals, communication-enabled appliances may be caused to go into asuper-low power mode. For example, a water dispenser at home may shutitself down into a super-low-power mode when the user is asleep or outfor work, and may start cooling/heating water once the user's activityat home is expected.

Restaurant Recommendation System Based on Location and Activity

Aggregation of real-time biometric signals and location information maybe used to create an educated-guess on one or multiple users' needs fora given time, e.g., ionized drink. Combining this guessed need withhistorical user data on the user's activity levels, activity types,activity time, and activity durations, as well as food intake datalogged by the users, an app on a smart phone and/or smart watch mayrecommend a restaurant that would meet the user's life-style and currentneed.

For example, a user who just finished a six mile circuit may launch thisapp. The app may know that this person maintained a high activity levelfor the past hour, and thus determine that the person may be dehydrated.From the historical user data, the app may also know, for example, thatthe user's diet is heavy on vegetables but low in sugar. With anoptimization algorithm that considers the user's current location, priceranges, and other factors mentioned above, the app may recommend arestaurant that offers smoothies, for example.

Blood Glucose Level and Heart Rate

Biometric monitoring devices that continuously measure biometric signalsmay provide meaningful information on preconditions of, progresstowards, and recoveries from diseases. Such biometric monitoring devicesmay have sensors and run algorithms accordingly to measure and calculatebiometric signals such as heart rate, heart rate variability, stepstaken, calories burned, distance traveled, weight and body fat, activityintensity, activity duration and frequency, etc. In addition to themeasured biometric signals, food intake logs provided by users may beused.

In one embodiment, a biometric monitoring device may observe heart rateand its changes over time, especially before and after a food intakeevent or events. It is known that heart rate is affected by blood sugarlevel, whereas it is well known that high blood sugar level is apre-diabetic condition. Thus, mathematical models that describe therelation between time elapsed (after food intake) and blood sugar levelmay be found via statistical regression, where data are collected fromnormal, pre-diabetic, and diabetic individuals to provide respectivemathematical models. With the mathematical models, one may predictwhether an individual with specific heart rate patterns is healthy,pre-diabetic, or diabetic.

Knowing that many heart failures are associated with pre-diabetic ordiabetic conditions, it is possible to further inform users of biometricmonitoring devices with possible heart failures, e.g., coronary heartdisease, cerebrovascular disease and peripheral vascular disease etc.,of such risks based on their biometric data.

Users' activity intensity, type, duration, and frequency may also betaken into account, when developing the mathematical models, as anargument that controls “probability” of the disease onset, usingrecommended exercise guidelines such as guidelines provided by AmericanHeart Association (http://www.heart.org/). Many guidelines on nutritionand weight management are also available in academia and to the generalpublic to prevent cardiovascular and diabetic disease. Such guidelinesmay be incorporated into the mathematical models with the user dataaccumulated over time, such as ingredients of the food that the usersconsumed, and weight and body fat trends.

If users have set their family members as their friends on a socialnetwork site, which stores and displays biometric data, the likelihoodof the family members getting a disease may also be analyzed and theusers informed of the results.

In addition to informing users regarding a potential development ofdisease, recommended life-style including exercise regime and recipeswith healthier ingredients and methods of preparation may be provided tothe users.

Sport Metric Acquisition Using a Sensor Device

In some embodiments, a sensor may be mounted on a racket, e.g., tennisracket, to help to measure the different strokes of the player. This maybe applicable to most, if not all, racket sports including, but notlimited to, tennis, racquetball, squash, table tennis, badminton,lacrosse, etc., as well as sports played with a bat like baseball,softball, cricket, etc. Similar techniques may also be used to measuredifferent aspects of golf. Such a device can be mounted on the base ofthe racket, on the handle or on the shock absorber typically mounted onthe strings. This device may have various sensors like an accelerometer,gyroscope, magnetometer, strain sensor, and/or microphone. The data fromthese sensors may either be stored locally or transmitted wirelessly toa host system on a smartphone or other wireless receiver.

In some embodiments of a biometric monitoring device, a wrist mountedbiometric monitoring device including an accelerometer, gyroscope,magnetometer, microphone, etc. may perform similar analysis of theuser's game or motions. This biometric monitoring device may take theform of a watch or other band worn on the user's wrist. Racket- orbat-mounted sensors that measure or detect the moment of impact betweenthe bat or racket and the ball and wirelessly transmit such data to thewrist-mounted biometric monitoring device may be used to improveaccuracy of such algorithms by accurately measuring the time of impactwith the ball.

Both wrist and racket-/bat-mounted devices may help measure differentaspects of the user's game including, but not limited to, stroke-type(forehand, backhand, serve, slice, etc.), number of forehands, number ofbackhands, ball spin direction, topspin, service percentage, angularvelocity of racket head, backswing, shot power, shot consistency, etc.The microphone or the strain sensor may be used in addition to theaccelerometer to identify the moment at which the ball impacts theracket/bat. In cricket and baseball, such a device may measure thebackswing, the angular velocity of the bat at the time of impact, thenumber of shots on the off-side vs. leg-side (cricket). It may alsomeasure the number of swings and misses and the number of defensive vs.offensive strokes. Such a device may also have a wireless transmitter totransmit such statistics in real time to a scoreboard or to individualdevices held by spectators.

The wrist- or racket-mounted device may have a small number of buttons(e.g., two) that may be used by the player to indicate when a volley iswon or when an unforced error occurs. This will allow the algorithm tocalculate the fraction of winners and unforced errors that are forehandsvs. backhands. The algorithm may also keep track of the number of acesvs. double-faults in tennis. If both players use such a system, thesystem may also automatically keep track of the score.

Indirect Metric Estimation

Bicycle computers typically measure a variety of metrics including, butnot limited to, speed, cadence, power, and wind speed. In the case thatthe portable monitoring device does not measure these metrics or is notin communication with devices which may be able to supply these metrics,these and other metrics may be inferred using the sensors that theportable biometric monitoring device does have. In one embodiment, theportable biometric monitoring device may measure heart rate. It may usethis measurement to infer/estimate the amount of power that the user isoutputting. Other metrics such as the user's age, height, and weight mayhelp inform the power measurement. Additional sensor data such asGPS-measured speed, altitude gain/descent, bicycle attitude (so as themeasure the incline or decline of a slope), and accelerometer signalsmay be used to further inform the power estimate. In one embodiment, anapproximately linear relationship between heart rate and power outputmay be used to calculate the user's power output.

In one embodiment, a calibration phase may occur where the user takesdata from the portable biometric monitoring device and a secondarydevice that may be used during calibration as a baseline but not be usedat a later time (e.g., a power meter). This may allow a relationshipbetween sensor data measured by the portable monitoring device andsensor data measured by the secondary device data to be determined. Thisrelationship may then be used when the secondary device is not presentto calculate estimated values of data that is explicitly provided by thesecondary device but not by the biometric monitoring device.

Activity Based Automatic Scheduling

In one embodiment, the day's travel requirements (to work, from work,between meetings) may be scheduled for the user based on the informationin their calendar (or emails or text messages etc.), with the aim ofmeeting daily activity goal(s) or long term activity goal(s). The user'shistorical data may be used to help plan both meeting the goal(s) andalso the transit time required. This feature may be combined withfriends or colleagues. The scheduling may be done such that a user maymeet a friend along the way as they walk to work, or meet a colleague onthe way to a meeting (the user might need to set a rendezvous point,though). If there is real-time communication between biometricmonitoring devices of the user and the user's friend, the user may bedirected to walk a longer route if data from the friend's biometricmonitoring device indicates that their friend is running late.

In another embodiment, walking/running/fitness routes may be suggestedto the user based (in whole or in part) on their proximity to the user.The data for such recommendations could also or additionally be based onGPS info from other users. If there is real-time communication, the usermay be directed to a busy route or a quiet route as preferred. Knowingheart rate and basic fitness information about other users may allow thesystem to suggest a route to match a user's fitness level and thedesired exercise/exertion level. Again this information may be used forplanning/guiding a user to longer term activity/fitness goals.

Location/Context Sensing and Applications

Through one or more methods, embodiments of the biometric monitoringdevices disclosed herein may have sensors that can determine or estimatethe location and or context (e.g. in a bus, at home, in a car) of thebiometric monitoring device. Purpose-built location sensors such as GPS,GLONASS, or other GNSS (Global Navigation Satellite System) sensors maybe used. Alternatively, location may be inferred, estimated or guessedusing less precise sensors. In some embodiments in which it is difficultto know the user's location, user input may aid in the determination oftheir location and or context. For example, if sensor data makes itdifficult to determine if a user was in a car or a bus, the biometricmonitoring device or a portable communication device in communicationwith the biometric monitoring device or a cloud server which is incommunication with the biometric monitoring device may present a queryto the user asking them if they took the bus today or took a car.Similar queries may occur for locations other than vehicular contexts.For example, if sensor data indicate that the user completed a vigorousworkout, but there is no location data that indicates that the user wentto a gym, the user may be asked if they went to the gym today.

Vehicular Transportation Detection

In some embodiments, sensors of the biometric monitoring device and/or aportable electronic device in communication with the biometricmonitoring device and/or a server which communicates with the biometricmonitoring device may be used to determine what type of vehicle (if any)the user is, or was, in. Note that in the embodiments below, a sensor inone or more biometric monitoring devices and/or portable electronicdevices may be used to sense the relevant signal. Also note that whilespecific network protocols such as WiFi or Bluetooth may be used in thefollowing descriptions, one or more alternative protocols such as RFID,NFC, or cellular telephony may also be used.

In one embodiment, the detection of a Bluetooth device associated with avehicle may be used to infer that the user is in a vehicle. For example,a user may have a car that has a Bluetooth multimedia system. When theuser gets close enough to their car for a long enough period of time,the sensor device may recognize the Bluetooth identification of themultimedia system and assume that the user is in the car. Data fromother sensors may be used to corroborate the assumption that the user isin the vehicle. Examples of data or signals from other sensors that maybe used to confirm that the user is in a car include a GPS speedmeasurement that is higher than 30 mph and accelerometer signals thatare characteristic of being in a car. Information intrinsic to theBluetooth ID may be used to determine that it is a Wi-Fi router of avehicle or type of vehicle. For example, the Bluetooth ID of a router ina car may be “Audi In-Car Multimedia.” The keyword “Audi” or “Car” maybe used to guess that the router is associated with a vehicle type of“car.” Alternatively, a database of Bluetooth ID's and their associatedvehicles may be used.

In one embodiment, a database of Bluetooth ID's and their associatedvehicles may be created or updated by the user of a biometric monitoringdevice or through portable communication device data. This may be donewith and/or without the aid of user input. In one embodiment if abiometric monitoring device can determine whether or not it is in avehicle, vehicle type, or specific vehicle without the use of BluetoothID, and it encounters a Bluetooth ID that moves with the vehicle, it maysend the Bluetooth ID and information regarding the vehicle to a centraldatabase to be catalogued as a Bluetooth ID that corresponds with avehicle. Alternatively, if a user inputs information about the vehiclethey are in or were in at a previous point in time and there is aBluetooth ID that was encountered during or close to the time that theuser indicated they were in the vehicle, the Bluetooth ID and vehicleinformation may be sent to a central database and associated with oneanother.

In another embodiment, the detection of a Wi-Fi device associated with avehicle may be used to infer that the user is in that vehicle or type ofvehicle. Some trains, buses, airplanes, cars, and other vehicles haveWi-Fi routers in them. The SSID of the router may be detected and usedto infer or aid an inference that a user is in a specific vehicle ortype of vehicle.

In one embodiment, a database of SSID's and their associated vehiclesmay be created or updated with the user of a biometric monitoring deviceor through portable communication device data. This may be done withand/or without the aid of user input. In one embodiment, if a biometricmonitoring device can determine whether or not it is in a vehicle,vehicle type, or specific vehicle without the use of an SSID, and itencounters an SSID that moves with the vehicle, the biometric monitoringdevice may send the SSID and information regarding the vehicle to acentral database to be catalogued as an SSID that corresponds with avehicle. Alternatively, if a user inputs information about the vehiclethey are in or were in at a previous point in time and there is an SSIDthat was encountered during or close to the time that the user indicatedthey were in the vehicle, the SSID and vehicle information may be sentto a central database and associated with one another.

In another embodiment of a biometric monitoring device, location sensorsmay be used to determine the track of a user. This track may then becompared to a database of routes for different modes of transit. Modesof transit may include, but are not limited to walking, running, biking,driving, taking a bus, taking a train, taking a tram, taking the subway,and/or motorcycling. If the user's track corresponds well with a routeof a specific mode of transit, it may be assumed that the user used thatmode of transit for the period of time that it took them to traverse theroute. Note that the speed with which the route or sections of the routewere completed may improve the guess of the mode of transit. Forexample, a bus and a car may both be able to take the same route, butthe additional stopping of the bus at bus stops may allow the device todetermine that the user was taking a bus rather than a car. Similarly,the discrimination between biking and driving a route may be aided bythe typical difference of speed between the two. This difference inspeed may also depend on the time of day. For example, some routes maybe slower by car during rush hour.

In another embodiment, a biometric monitoring device may be able todetect that the user is in or near a vehicle based on measurements ofthe magnetic field of vehicle. In some embodiments, the magnetic fieldsignature of a location typically associated with the vehicle (e.g.,train station, subway station, bus stop, car garage) may also be used toinfer that the user is currently in, will be, or has been in a vehicle.The magnetic field signature may be time invariant or time varying.

If it is determined that the user was indeed in a vehicle for a periodof time, other metrics about the user may be modified to reflect such astatus. In the case that the biometric monitoring device and/or portableelectronic device can measure activity metrics such as steps taken,distance walked or run, altitude climbed, and/or calories burned, thesemetrics may be modified based on information about vehicular travel. Ifany steps taken or altitude climbed were incorrectly logged during thetime that the user is in a vehicle, they may be removed from the log ofmetrics about the user. Metrics derived from the incorrectly loggedsteps taken or altitude climbed such as distance travelled and caloriesburned may also be removed from the log of metrics about the user. Inthe case that it can be determined in real-time or near real-timewhether or not the user is in a vehicle, the sensors detecting metricswhich should not be measured while in a vehicle (e.g. steps taken,stairs climbed) may be turned off or algorithms which are used tomeasure these metrics may be turned off to prevent incorrectly loggedmetrics (as well to save power). Note that metrics regarding vehicle usesuch as type of vehicle taken, when it was taken, which route was taken,and how long the trip took may be recorded and used later to present theuser with this data and/or to correct other activity and physiologicalmetrics about the user.

Location Sensing Using Bluetooth

Methods similar to those described above may also be used by a biometricmonitoring device to determine when the user comes into proximity ofstatic locations. In one embodiment, Bluetooth ID's from computers(e.g., tablet computers) at restaurants or stores may be used todetermine the user's location. In another embodiment, semi-fixedBluetooth ID's from portable communication devices (e.g., smartphones)may be used to determine a user's location. In the case of semi-fixedBluetooth ID sources, multiple Bluetooth ID's may be need to reach anacceptable level of confidence of the location of the user. For example,a database of Bluetooth ID's of the coworkers of a user may be created.If the user is within range of several of these Bluetooth ID's duringtypical working hours, it may be assumed that the user is at work. Thedetection of other Bluetooth ID's may also be used to record when twousers meet up. For example, it may be determined that a user went for arun with another user by analyzing pedometer data and Bluetooth ID's.Similar such concepts are discussed in further detail in U.S.Provisional Patent Application No. 61/948,468, filed Mar. 5, 2014, andpreviously incorporated by reference with regard to such concepts.

Uncertainty Metric for GPS Based on Location

When fusing sensor signals with GPS signal to estimate informativebiometrics, such as steps, live pace, speed, or trajectory of trips,quality of the GPS signal is often very informative. However, GPS signalquality is known to be time-varying, and one of the factors that affectsthe signal quality is environmental surroundings.

Location information may be used to estimate GPS signal quality. Aserver may store a map of area types, where the area types arepre-determined by number and kind of objects that deteriorate GPSsignals. The types may, for example, be: large building area, smallbuilding area, open area, side-by-water area, and forested area. Thesearea types are then queried when GPS sensor gets turned on with its veryfirst few location estimates, which are expected to be rough andinaccurate. With the rough GPS estimates of the location, possible typesof areas may be returned, and these area types may then be taken intoaccount in the calculation of the GPS signal quality and reliability.

For example, if a user is in or near an urban canyon (an area surroundby tall buildings) such as downtown San Francisco, a low certainty maybe associated with any GNSS location measurements. This certainty may beused later by algorithms that attempt to determine the user's track,speed, and/or elevation based on, at least in part, GPS data.

In one embodiment, a database of location and GPS signal quality may becreated automatically using data from one or more GNSS sensors. This maybe automatically performed by comparing the GNSS tracks with a map ofstreets and seeing when the GNSS sensors show characteristics of a usertravelling along a street (e.g., having a speed of 10 mph or higher),but their track is not located on a road. The database of GPS certaintybased on approximate location may also be inferred from maps showingwhere there are tall buildings, canyons, or dense forests.

Location Sensing using Vehicular GNSS and/or Dead Reckoning

Many vehicles have integrated GNSS navigation systems. Users of vehiclesthat don't have integrated GNSS navigations systems often buy a GNSSnavigation system for their car that is typically mountednon-permanently in the driver's field of view. In one embodiment, aportable biometric monitoring device may be able to communicate with thevehicle's GNSS system. In the case where the portable biometricmonitoring device is also used to track location, it may receivelocation information from the vehicle GNSS. This may enable thebiometric monitoring device to turn off its own GNSS sensor (in the casethat it has one), therefore reducing its power consumption.

In addition to GNSS location detection, a vehicle may be able totransmit data about its steering wheel orientation and/or itsorientation with respect to the earth's magnetic field in addition toits speed as measured using the tire size and tire rotational velocity.This information may be used to perform dead-reckoning to determine atrack and/or location in the case that the vehicle does not have a GNSSsystem or the vehicle's GNSS system cannot get a reliable locationmeasurement. Dead-reckoning location information may supplement GNSSsensor data from the biometric monitoring device. For example, thebiometric monitoring device may reduce the frequency with which itsamples GNSS data and fill in the gap between GNSS location data withlocations determined through dead reckoning.

Step counter data fusion with satellite-based location determination

In some implementations of a biometric monitoring device, data fromvarious different sensors may be fused together to provide new insightsas to activities of the wearer of the biometric monitoring device. Forexample, data from an altimeter in the biometric monitoring device maybe combined with step count data obtained by performing peak detectionanalysis on accelerometer data from an accelerometer of the biometricmonitoring device to determine when the wearer of the biometricmonitoring device is, for example, climbing stairs or walking uphill (asopposed to riding an elevator or an escalator or walking across flatground).

In another example of sensor data fusion, data from a step counter suchas that discussed above may be combined with distance measurementsderived from GPS data to provide a refined estimate of total distancetraveled within a given window. For example, GPS-based distance or speeddata may be combined with step-counter-based distance or speed (usingsteps taken multiplied by stride length, for example) using a Kalmanfilter in order to obtain a refined distance estimate that may be moreaccurate than either the GPS-based distance or speed measurement or thestep-counter-based distance or speed measurement alone. In anotherimplementation, a GPS-based distance measurement may be filtered using asmoothing constant that is a function of the step rate as measured by,for example, an accelerometer. Such implementations are discussedfurther in U.S. Provisional Patent Application No. 61/973,614, filedApr. 1, 2014, which was previously incorporated herein by reference inthe “Cross-Reference to Related Applications” section and which is againhereby incorporated by reference with respect to content directed atdistance or speed estimation refinement using data from satellite-basedlocation systems and step count sensors.

Biometric and Environmental/Exercise Performance Correlation

Some embodiments of portable monitoring devices described herein maydetect a variety of data including biometric data, environmental data,and activity data. All of this data may be analyzed or presented to auser to facilitate analysis of or correlation between two or more typesof data. In one embodiment, a user's heart rate may be correlated to carspeed, biking speed, running speed, swimming speed or walking speed. Forexample, the user may be presented with a graph that plots biking speedon the X axis and heart rate on the Y axis. In another example, a user'sheart rate may be correlated to music that they were listening to. Thebiometric monitoring device may receive data regarding what music theuser was listening to through a wireless connection (e.g., Bluetooth) toa car radio. In another embodiment, the biometric monitoring device mayalso function as a music player itself, and therefore can record whichsong was played when.

UV Exposure Detection

In one embodiment, the biometric monitoring device may have the abilityto monitor an individual's exposure to UV radiation. UVA and UVB may bemeasured with one or multiple sensors. For example, a photodiode havinga bandpass filter which passes only UVA may detect UVA exposure and aphotodiode having a bandpass filter which passes only UVB may detect UVBexposure. The user's skin pigmentation may also be measured using acamera or reflectometer (light emitter and light detector whichdetermines the efficiency with which light is reflected off the skin).Using UVA, UVB, and skin pigmentation data, the biometric monitoringdevice may provide a user with information regarding the amount of UVexposure they have been subjected to. The biometric monitoring devicemay also provide estimates or alarms regarding over exposure to UV,potential for sunburn, and potential for increasing their risk of skincancer.

Screen Power Saving Using User Presence Sensors

The portable biometric monitoring device may have one or more a displaysto present information to the user. In one embodiment sensors on thebiometric monitoring device may determine the user is using thebiometric monitoring device and/or wearing the biometric monitoringdevice to determine the state of the display. For example, a biometricmonitoring device having a PPG sensor may use the PPG sensor as aproximity sensor to determine when the user is wearing the biometricmonitoring device. If the user is wearing the biometric monitoringdevice, the state of the screen (e.g. a color LCD screen) may be changedto “on” or “standby” from its typical state of being off.

Power Conservation with Respect to Satellite-Based LocationDetermination Systems

In some implementations, certain systems included in a biometricmonitoring device may consume relatively large amounts of power comparedto other systems in the biometric monitoring device. Due to the smallspace constraints of many biometric monitoring devices, this mayseriously affect overall battery charge life for the biometricmonitoring device. For example, in some biometric monitoring devices, asatellite-based location determination system may be included. Each timethe satellite-based location determination system is used to obtain aposition fix using data from the GPS satellite constellation, it usespower drawn from the biometric monitoring device battery. The biometricmonitoring device may be configured to alter the frequency with whichthe satellite-based location determination system obtains a location fixbased on data from one or more sensors of the biometric monitoringdevice. This adaptive location fix frequency functionality may helpconserve power while still allowing the satellite-based locationdetermination system to provide location fixes at useful intervals (whenappropriate).

For example, if a biometric monitoring device has an ambient lightsensor, data from the ambient light sensor may be used to determinewhether the lighting conditions indicate that the biometric monitoringdevice is likely indoors as opposed to outdoors. If indoors, thebiometric monitoring device may cause the location fix frequency to beset to a level that is lower than the location fix frequency that may beused when the lighting conditions appear to indicate that the biometricmonitoring device is outdoors. This has the effect of decreasing thenumber of location fixes that are attempted when the biometricmonitoring device is indoors and thus less likely to obtain a goodlocation fix using a satellite-based location determination system.

In another example, if motion sensors of the biometric monitoring deviceindicate that the wearer of the biometric monitoring device issubstantially stationary, e.g., sleeping or generally not moving morethan a few feet every minute, the location fix frequency of thesatellite-based location determination system may be set to a lowerlevel than if the motion sensors indicate that the wearer of thebiometric monitoring device is in motion, e.g., walking or running fromone location to another, e.g., moving more than a few feet.

In yet another example, the biometric monitoring device may beconfigured to determine if the biometric monitoring device is actuallybeing worn by a person—if not, the biometric monitoring device may setthe location fix frequency to a lower level than if the biometricmonitoring device is actually being worn. Such determinations regardingwhether or not the biometric monitoring device is being worn may bemade, for example, when motion data collected from motion sensors of thebiometric monitoring device indicate that the biometric monitoringdevice is substantially immobile, e.g., not even demonstrating smallmovements experienced by biometric monitoring devices when the wearer issleeping or sedentary, or when data, for example, from a pulse waveformsensor indicates that no heart rate is detected. For optical pulsewaveform sensors, if there is little or no change in the amount of lightdetected by the light detection sensor when the light source is turnedon and off, this may be indicative of the fact that the pulse waveformsensor is not pressed against a person's skin and that, by inference,the biometric monitoring device is not being worn. Such adaptivesatellite-based location determination system fix frequency concepts arediscussed in more detail in U.S. Provisional Patent Application No.61/955,045, filed Mar. 18, 2014, which was previously incorporatedherein by reference in the “Cross-Reference to Related Applications”section and which is again hereby incorporated by reference with respectto content directed at power conservation in the context ofsatellite-based location determination systems.

It is to be understood that biometric monitoring devices, in addition toincluding the features discussed below in more detail, may also includeone or more features or functionalities discussed above or discussed inthe various applications incorporated by reference in the abovediscussion. Such implementations are to be understood as being withinthe scope of this disclosure.

There are many concepts and embodiments described and illustratedherein. While certain embodiments, features, attributes, and advantageshave been described and illustrated herein, it should be understood thatmany others, as well as different and/or similar embodiments, features,attributes and advantages are apparent from the description andillustrations. As such, the above embodiments are merely provided by wayof example. They are not intended to be exhaustive or to limit thisdisclosure to the precise forms, techniques, materials and/orconfigurations disclosed. Many modifications and variations are possiblein light of this disclosure. It is to be understood that otherembodiments may be utilized and operational changes may be made withoutdeparting from the scope of the present disclosure. As such, the scopeof the disclosure is not limited solely to the description above becausethe descriptions of the above embodiments have been presented for thepurposes of illustration and description.

Importantly, the present disclosure is neither limited to any singleaspect nor embodiment, nor to any combinations and/or permutations ofsuch aspects and/or embodiments. Moreover, each of the aspects of thepresent disclosure, and/or embodiments thereof, may be employed alone orin combination with one or more of the other aspects and/or embodimentsthereof. For the sake of brevity, many of those permutations andcombinations will not be discussed and/or illustrated separately herein.

What is claimed is:
 1. A biometric monitoring device for measuring arterial stiffness comprising: (a) a wearable fixing structure configured to attach to a user and/or a user's apparel in a manner allowing the user to wear the biometric monitoring device while performing activities; (b) an inertial sensor configured to generate inertial data measuring movement experienced by the biometric monitoring device; (c) a photoplethysmogram (“PPG”) sensor to generate PPG sensor data; and (d) one or more processors configured to: (i)(1) control the biometric monitoring device to operate in a first mode when the user is wearing the biometric monitoring device and performing a first activity, wherein while in the first mode the processors do not obtain the PPG sensor data or obtain the PPG sensor data at a sampling rate lower than in a second mode, (i)(2) obtain the inertial data from the inertial sensor when the user is wearing the biometric monitoring device and performing a second activity, and determine from the inertial data that the biometric monitoring device has experienced movement below a movement threshold for a period of time and the inertial data matches an orientation profile, (ii) responsive to the determination of (i)(2), control the biometric monitoring device to operate in the second mode to obtain the PPG sensor data from the PPG sensor, (iii) filter the PPG sensor data using information obtained from the inertial data, and (iv) determine one or more morphological features of a pulse waveform using the filtered PPG sensor data without using electrode-based heart sensor data, and/or transmit the filtered PPG sensor data to a device configured to determine the one or more morphological features of the pulse waveform using the filtered PPG sensor data without using electrode-based heart sensor data, wherein the one or more morphological features are related to arterial stiffness.
 2. The biometric monitoring device of claim 1, wherein the activity type is selected from the group consisting of resistance training, aerobic exercising, endurance training, sitting, working, and sleeping.
 3. The biometric monitoring device of claim 1, wherein while in the first mode the processors obtain the PPG sensor data at a sampling rate lower than in the second mode.
 4. The biometric monitoring device of claim 1, further comprising one or more sensors selected from the group consisting of a temperature sensor, a strain sensor, and a pressure sensor, wherein the one or more processors are further configured to perform wave normalization on the pulse waveform using temperature data from the temperature sensor or using pressure data from the strain sensor or the pressure sensor.
 5. The biometric monitoring device of claim 1, wherein the one or more processors are further configured to use the inertial data to reject motion artifact.
 6. The biometric monitoring device of claim 1, wherein the one or more processors are further configured to trigger repeatedly obtaining the PPG sensor data for determining pulse waveforms, while the user wears the biometric monitoring device.
 7. The biometric monitoring device of claim 6, wherein the one or more processors are further configured to determine the user's heart rate using the PPG sensor data and trigger obtaining the PPG sensor data for a number of pulse waveforms, wherein the number of pulse waveforms depends at least partly on the user's heart rate.
 8. The biometric monitoring device of claim 6, wherein the one or more processors are configured to determine the user's respiration rate and trigger obtaining the PPG sensor data for a number of pulse waveforms, wherein the number of pulse waveforms depends at least partly on the user's respiration rate.
 9. The biometric monitoring device of claim 6, wherein the one or more processors are configured to trigger obtaining the PPG sensor data at a schedule based on activity types or locations of the user.
 10. The biometric monitoring device of claim 1, wherein the one or more processors are further configured to analyze the pulse waveform to obtain an estimate of the user's arterial stiffness.
 11. A biometric monitoring device for measuring arterial stiffness comprising: (a) a wearable fixing structure configured to attach to a user and/or a user's apparel in a manner allowing the user to wear the biometric monitoring device while performing activities; (b) an inertial sensor configured to generate inertial data measuring movement experienced by the biometric monitoring device; (c) a photoplethysmogram (“PPG”) sensor to generate PPG sensor data; and (d) one or more processors configured to: (i) obtain the inertial data from the inertial sensor, (ii) determine the user's heart rate, (iii) trigger repeatedly obtaining the PPG sensor data for determining a number of pulse waveforms while the user wears the biometric monitoring device, wherein the number of pulse waveforms depends at least partly on the user's heart rate; (iv) filter the PPG sensor data using information obtained from the inertial data, and (v) determine one or more morphological features of a pulse waveform derived from the filtered PPG sensor data, and/or transmit the filtered PPG sensor data to a device configured to determine the one or more morphological features of the pulse waveform from the filtered PPG sensor data, wherein the one or more morphological features are related to arterial stiffness.
 12. A biometric monitoring device for measuring arterial stiffness comprising: (a) a wearable fixing structure configured to attach to a user and/or a user's apparel in a manner allowing the user to wear the biometric monitoring device while performing activities; (b) an inertial sensor configured to generate inertial data measuring movement experienced by the biometric monitoring device; (c) a photoplethysmogram (“PPG”) sensor to generate PPG sensor data; and (d) one or more processors configured to: (i) obtain the inertial data from the inertial sensor, (ii) determine the user's respiration rate using the PPG sensor data, (iii) trigger repeatedly obtaining the PPG sensor data for determining a number of pulse waveforms while the user wears the biometric monitoring device, wherein the number of pulse waveforms depends at least partly on the user's respiration rate; (iv) filter the PPG sensor data using information obtained from the inertial data, and (v) determine one or more morphological features of a pulse waveform derived from the filtered PPG sensor data, and/or transmit the filtered PPG sensor data to a device configured to determine the one or more morphological features of the pulse waveform from the filtered PPG sensor data, wherein the one or more morphological features are related to arterial stiffness. 